var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"host\":\"bibbase.org\",\"ssl\":false},\n      data: [{\"title\":\"Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder\",\"type\":\"article\",\"year\":\"2018\",\"pages\":\"1649\",\"volume\":\"8\",\"websites\":\"http://dx.doi.org/10.1038/s41598-018-19888-9\",\"publisher\":\"Springer US\",\"id\":\"e30f810e-83d0-3682-905a-bc50e7462ab6\",\"created\":\"2019-02-22T10:25:09.690Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2019-02-22T10:29:38.343Z\",\"read\":\"true\",\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Carr, Oliver and Saunders, Kate E. A. and Tsanas, Athanasios and Palmius, Niclas and Geddes, John R. and Foster, Russell and Goodwin, Guy M. and De Vos, Maarten\",\"doi\":\"10.1038/s41598-018-19888-9\",\"journal\":\"Scientific reports\",\"bibtex\":\"@article{\\n title = {Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder},\\n type = {article},\\n year = {2018},\\n pages = {1649},\\n volume = {8},\\n websites = {http://dx.doi.org/10.1038/s41598-018-19888-9},\\n publisher = {Springer US},\\n id = {e30f810e-83d0-3682-905a-bc50e7462ab6},\\n created = {2019-02-22T10:25:09.690Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2019-02-22T10:29:38.343Z},\\n read = {true},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Carr, Oliver and Saunders, Kate E. A. and Tsanas, Athanasios and Palmius, Niclas and Geddes, John R. and Foster, Russell and Goodwin, Guy M. and De Vos, Maarten},\\n doi = {10.1038/s41598-018-19888-9},\\n journal = {Scientific reports}\\n}\",\"author_short\":[\"Carr,  O.\",\"Saunders,  K., E., A.\",\"Tsanas,  A.\",\"Palmius,  N.\",\"Geddes,  J., R.\",\"Foster,  R.\",\"Goodwin,  G., M.\",\"De Vos,  M.\"],\"urls\":{\"Website\":\"http://dx.doi.org/10.1038/s41598-018-19888-9\"},\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"carr-saunders-tsanas-palmius-geddes-foster-goodwin-devos-variabilityinphaseandamplitudeofdiurnalrhythmsisrelatedtovariationofmoodinbipolarandborderlinepersonalitydisorder-2018\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment\",\"type\":\"article\",\"year\":\"2017\",\"keywords\":\"bipolar disorder,borderline personality disorder,depression,latent variable structure,mania,mhealth,mobile app,mood monitoring,outcome measures,patient reported\",\"pages\":\"e15\",\"volume\":\"4\",\"websites\":\"http://mental.jmir.org/2017/2/e15/\",\"id\":\"115fd00c-4e7c-3bcb-876d-f5c6a8f5daef\",\"created\":\"2019-02-22T10:25:09.733Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2019-02-22T10:25:09.733Z\",\"read\":\"true\",\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"BACKGROUND We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. OBJECTIVE The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. METHODS We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. RESULTS We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. CONCLUSIONS This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice.\",\"bibtype\":\"article\",\"author\":\"Tsanas, Athanasios and Saunders, Kate and Bilderbeck, Amy and Palmius, Niclas and Goodwin, Guy and De Vos, Maarten\",\"doi\":\"10.2196/mental.6917\",\"journal\":\"JMIR Mental Health\",\"number\":\"2\",\"bibtex\":\"@article{\\n title = {Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment},\\n type = {article},\\n year = {2017},\\n keywords = {bipolar disorder,borderline personality disorder,depression,latent variable structure,mania,mhealth,mobile app,mood monitoring,outcome measures,patient reported},\\n pages = {e15},\\n volume = {4},\\n websites = {http://mental.jmir.org/2017/2/e15/},\\n id = {115fd00c-4e7c-3bcb-876d-f5c6a8f5daef},\\n created = {2019-02-22T10:25:09.733Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2019-02-22T10:25:09.733Z},\\n read = {true},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {BACKGROUND We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. OBJECTIVE The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. METHODS We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. RESULTS We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. CONCLUSIONS This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice.},\\n bibtype = {article},\\n author = {Tsanas, Athanasios and Saunders, Kate and Bilderbeck, Amy and Palmius, Niclas and Goodwin, Guy and De Vos, Maarten},\\n doi = {10.2196/mental.6917},\\n journal = {JMIR Mental Health},\\n number = {2}\\n}\",\"author_short\":[\"Tsanas,  A.\",\"Saunders,  K.\",\"Bilderbeck,  A.\",\"Palmius,  N.\",\"Goodwin,  G.\",\"De Vos,  M.\"],\"urls\":{\"Website\":\"http://mental.jmir.org/2017/2/e15/\"},\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tsanas-saunders-bilderbeck-palmius-goodwin-devos-clinicalinsightintolatentvariablesofpsychiatricquestionnairesformoodsymptomselfassessment-2017\",\"role\":\"author\",\"keyword\":[\"bipolar disorder\",\"borderline personality disorder\",\"depression\",\"latent variable structure\",\"mania\",\"mhealth\",\"mobile app\",\"mood monitoring\",\"outcome measures\",\"patient reported\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder\",\"type\":\"article\",\"year\":\"2018\",\"pages\":\"79\",\"volume\":\"8\",\"id\":\"e2876a17-b154-3c03-850d-6524c5e45e2f\",\"created\":\"2019-02-22T10:25:09.736Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2019-02-22T10:29:38.355Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"© 2018 The Author(s). It has long been proposed that diurnal rhythms are disturbed in bipolar disorder (BD). Such changes are obvious in episodes of mania or depression. However, detailed study of patients between episodes has been rare and comparison with other psychiatric disorders rarer still. Our hypothesis was that evidence for desynchronization of diurnal rhythms would be evident in BD and that we could test the specificity of any effect by studying borderline personality disorder (BPD). Individuals with BD (n = 36), BPD (n = 22) and healthy volunteers (HC, n = 25) wore a portable heart rate and actigraphy device and used a smart-phone to record self-assessed mood scores 10 times per day for 1 week. Average diurnal patterns of heart rate (HR), activity and sleep were compared within and across groups. Desynchronization in the phase of diurnal rhythms of HR compared with activity were found in BPD (+3 h) and BD (+1 h), but not in HC. A clear diurnal pattern for positive mood was found in all subject groups. The coherence between negative and irritable mood and HR showed a four-cycle per day component in BD and BPD, which was not present in HC. The findings highlight marked de-synchronisation of measured diurnal function in both BD but particularly BPD and suggest an increased association with negative and irritable mood at ultradian frequencies. These findings enhance our understanding of the underlying physiological changes associated with BPD and BD, and suggest objective markers for monitoring and potential treatment targets. Improved mood stabilisation is a translational objective for management of both patient groups.\",\"bibtype\":\"article\",\"author\":\"Carr, O. and Saunders, K.E.A. and Bilderbeck, A.C. and Tsanas, A. and Palmius, N. and Geddes, J.R. and Foster, R. and De Vos, M. and Goodwin, G.M.\",\"doi\":\"10.1038/s41398-018-0125-7\",\"journal\":\"Translational Psychiatry\",\"bibtex\":\"@article{\\n title = {Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder},\\n type = {article},\\n year = {2018},\\n pages = {79},\\n volume = {8},\\n id = {e2876a17-b154-3c03-850d-6524c5e45e2f},\\n created = {2019-02-22T10:25:09.736Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2019-02-22T10:29:38.355Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {© 2018 The Author(s). It has long been proposed that diurnal rhythms are disturbed in bipolar disorder (BD). Such changes are obvious in episodes of mania or depression. However, detailed study of patients between episodes has been rare and comparison with other psychiatric disorders rarer still. Our hypothesis was that evidence for desynchronization of diurnal rhythms would be evident in BD and that we could test the specificity of any effect by studying borderline personality disorder (BPD). Individuals with BD (n = 36), BPD (n = 22) and healthy volunteers (HC, n = 25) wore a portable heart rate and actigraphy device and used a smart-phone to record self-assessed mood scores 10 times per day for 1 week. Average diurnal patterns of heart rate (HR), activity and sleep were compared within and across groups. Desynchronization in the phase of diurnal rhythms of HR compared with activity were found in BPD (+3 h) and BD (+1 h), but not in HC. A clear diurnal pattern for positive mood was found in all subject groups. The coherence between negative and irritable mood and HR showed a four-cycle per day component in BD and BPD, which was not present in HC. The findings highlight marked de-synchronisation of measured diurnal function in both BD but particularly BPD and suggest an increased association with negative and irritable mood at ultradian frequencies. These findings enhance our understanding of the underlying physiological changes associated with BPD and BD, and suggest objective markers for monitoring and potential treatment targets. Improved mood stabilisation is a translational objective for management of both patient groups.},\\n bibtype = {article},\\n author = {Carr, O. and Saunders, K.E.A. and Bilderbeck, A.C. and Tsanas, A. and Palmius, N. and Geddes, J.R. and Foster, R. and De Vos, M. and Goodwin, G.M.},\\n doi = {10.1038/s41398-018-0125-7},\\n journal = {Translational Psychiatry}\\n}\",\"author_short\":[\"Carr,  O.\",\"Saunders,  K.\",\"Bilderbeck,  A.\",\"Tsanas,  A.\",\"Palmius,  N.\",\"Geddes,  J.\",\"Foster,  R.\",\"De Vos,  M.\",\"Goodwin,  G.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"carr-saunders-bilderbeck-tsanas-palmius-geddes-foster-devos-etal-desynchronizationofdiurnalrhythmsinbipolardisorderandborderlinepersonalitydisorder-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\",\"carr, o\":\"https://oliver-carr.github.io/\"}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Detecting Bipolar Depression from Geographic Location Data\",\"type\":\"article\",\"year\":\"2017\",\"pages\":\"1761-1771\",\"volume\":\"64\",\"websites\":\"http://ieeexplore.ieee.org/document/7676335/\",\"id\":\"ffc7e80b-6f1d-3422-b1e0-56f67193e1d9\",\"created\":\"2019-02-22T10:25:09.737Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2019-02-22T10:25:09.737Z\",\"read\":\"true\",\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Palmius, Niclas and Tsanas, Athanasios and Saunders, Kate E. A. and Bilderbeck, Amy C. and Geddes, John R. and Goodwin, Guy M. and De Vos, Maarten\",\"doi\":\"10.1109/TBME.2016.2611862\",\"journal\":\"IEEE Transactions on Biomedical Engineering\",\"number\":\"8\",\"bibtex\":\"@article{\\n title = {Detecting Bipolar Depression from Geographic Location Data},\\n type = {article},\\n year = {2017},\\n pages = {1761-1771},\\n volume = {64},\\n websites = {http://ieeexplore.ieee.org/document/7676335/},\\n id = {ffc7e80b-6f1d-3422-b1e0-56f67193e1d9},\\n created = {2019-02-22T10:25:09.737Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2019-02-22T10:25:09.737Z},\\n read = {true},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Palmius, Niclas and Tsanas, Athanasios and Saunders, Kate E. A. and Bilderbeck, Amy C. and Geddes, John R. and Goodwin, Guy M. and De Vos, Maarten},\\n doi = {10.1109/TBME.2016.2611862},\\n journal = {IEEE Transactions on Biomedical Engineering},\\n number = {8}\\n}\",\"author_short\":[\"Palmius,  N.\",\"Tsanas,  A.\",\"Saunders,  K., E., A.\",\"Bilderbeck,  A., C.\",\"Geddes,  J., R.\",\"Goodwin,  G., M.\",\"De Vos,  M.\"],\"urls\":{\"Website\":\"http://ieeexplore.ieee.org/document/7676335/\"},\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"palmius-tsanas-saunders-bilderbeck-geddes-goodwin-devos-detectingbipolardepressionfromgeographiclocationdata-2017\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Investigating Voice as a Biomarker for leucine-rich repeat kinase 2-Associated Parkinson’s Disease\",\"type\":\"article\",\"year\":\"2018\",\"pages\":\"503-510\",\"volume\":\"8\",\"id\":\"3f57ab1c-f2ea-3e53-beb6-196a618d25fe\",\"created\":\"2019-02-22T10:49:29.986Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2019-02-22T11:11:42.667Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations ('aaah' sounds) were recorded from 7 individuals with LRRK2-associated Parkinson's disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts.\",\"bibtype\":\"article\",\"author\":\"Arora, Siddharth and Visanji, Naomi P and Mestre, Tiago A and Tsanas, Athanasios and Aldakheel, Amaal and Connolly, Barbara S and Gasca-salas, Carmen and Kern, Drew S and Jain, Jennifer and Slow, Elizabeth J and Faust-Socher, Achinoam and Lang, Anthony E and Little, Max A and Marras, Connie\",\"doi\":\"10.3233/JPD-181389\",\"journal\":\"Journal of Parkinson's Disease\",\"number\":\"4\",\"bibtex\":\"@article{\\n title = {Investigating Voice as a Biomarker for leucine-rich repeat kinase 2-Associated Parkinson’s Disease},\\n type = {article},\\n year = {2018},\\n pages = {503-510},\\n volume = {8},\\n id = {3f57ab1c-f2ea-3e53-beb6-196a618d25fe},\\n created = {2019-02-22T10:49:29.986Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2019-02-22T11:11:42.667Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations ('aaah' sounds) were recorded from 7 individuals with LRRK2-associated Parkinson's disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts.},\\n bibtype = {article},\\n author = {Arora, Siddharth and Visanji, Naomi P and Mestre, Tiago A and Tsanas, Athanasios and Aldakheel, Amaal and Connolly, Barbara S and Gasca-salas, Carmen and Kern, Drew S and Jain, Jennifer and Slow, Elizabeth J and Faust-Socher, Achinoam and Lang, Anthony E and Little, Max A and Marras, Connie},\\n doi = {10.3233/JPD-181389},\\n journal = {Journal of Parkinson's Disease},\\n number = {4}\\n}\",\"author_short\":[\"Arora,  S.\",\"Visanji,  N., P.\",\"Mestre,  T., A.\",\"Tsanas,  A.\",\"Aldakheel,  A.\",\"Connolly,  B., S.\",\"Gasca-salas,  C.\",\"Kern,  D., S.\",\"Jain,  J.\",\"Slow,  E., J.\",\"Faust-Socher,  A.\",\"Lang,  A., E.\",\"Little,  M., A.\",\"Marras,  C.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"arora-visanji-mestre-tsanas-aldakheel-connolly-gascasalas-kern-etal-investigatingvoiceasabiomarkerforleucinerichrepeatkinase2associatedparkinsonsdisease-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"High-sensitivity troponin in the evaluation of patients with suspected acute coronary syndrome: a stepped-wedge, cluster-randomised controlled trial\",\"type\":\"article\",\"year\":\"2018\",\"pages\":\"919-928\",\"volume\":\"392\",\"id\":\"09e81b82-c9d8-3b5f-a82b-2e271684e710\",\"created\":\"2019-02-22T10:49:30.089Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2019-02-22T10:49:30.089Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Shah, Anoop S.V. and Anand, Atul and Strachan, Fiona E. and Ferry, Amy V. and Lee, Kuan K. and Chapman, Andrew R. and Sandeman, Dennis and Stables, Catherine L and Adamson, Philip D and Andrews, Jack P M and Anwar, Mohamed S and Hung, John and Moss, Alistair J and O'Brien, Rachel and Berry, Colin and Findlay, Iain and Walker, Simon and Cruickshank, Anne and Reid, Alan and Gray, Alasdair and Collinson, Paul O and Apple, Fred S and McAllister, David A and Maguire, Donogh and Fox, Keith A A and Newby, David E and Tuck, Christopher and Harkess, Ronald and Parker, Richard A and Keerie, Catriona and Weir, Christopher J and Mills, Nicholas L and Investigators, on behalf of the HIGH-STEACS and Mills, Nicholas L and Strachan, Fiona E and Tuck, Christopher and Shah, Anoop S V and Anand, Atul and Ferry, Amy V and Lee, Kuan Ken and Chapman, Andrew R and Sandeman, Dennis and Adamson, Philip D and Stables, Catherine L and Marshall, Lucy and Stewart, Stacey D and Fujisawa, Takeshi and Vallejos, Catalina A and Tsanas, Athanasios and Hautvast, Mischa and McPherson, Jean and McKinlay, Lynn and Newby, David E and Fox, Keith A A and Berry, Colin and Walker, Simon and Weir, Christopher J and Ford, Ian and Gray, Alasdair and Collinson, Paul O and Apple, Fred S and Reid, Alan and Cruikshank, Anne and Findlay, Iain and Amoils, Shannon and McAllister, David A and Maguire, Donogh and Stevens, Jennifer and Norrie, John and Andrews, Jack P M and Adamson, Philip D and Moss, Alastair and Anwar, Mohamed S and Hung, John and Malo, Jonathan and Fischbacher, Colin M and Croal, Bernard L and Leslie, Stephen J and Keerie, Catriona and Parker, Richard A and Walker, Allan and Harkess, Ronnie and Wackett, Tony and Armstrong, Roma and Flood, Marion and Stirling, Laura and MacDonald, Claire and Sadat, Imran and Finlay, Frank and Charles, Heather and Linksted, Pamela and Young, Stephen and Alexander, Bill and Duncan, Chris\",\"doi\":\"10.1016/s0140-6736(18)31923-8\",\"journal\":\"The Lancet\",\"number\":\"10151\",\"bibtex\":\"@article{\\n title = {High-sensitivity troponin in the evaluation of patients with suspected acute coronary syndrome: a stepped-wedge, cluster-randomised controlled trial},\\n type = {article},\\n year = {2018},\\n pages = {919-928},\\n volume = {392},\\n id = {09e81b82-c9d8-3b5f-a82b-2e271684e710},\\n created = {2019-02-22T10:49:30.089Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2019-02-22T10:49:30.089Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Shah, Anoop S.V. and Anand, Atul and Strachan, Fiona E. and Ferry, Amy V. and Lee, Kuan K. and Chapman, Andrew R. and Sandeman, Dennis and Stables, Catherine L and Adamson, Philip D and Andrews, Jack P M and Anwar, Mohamed S and Hung, John and Moss, Alistair J and O'Brien, Rachel and Berry, Colin and Findlay, Iain and Walker, Simon and Cruickshank, Anne and Reid, Alan and Gray, Alasdair and Collinson, Paul O and Apple, Fred S and McAllister, David A and Maguire, Donogh and Fox, Keith A A and Newby, David E and Tuck, Christopher and Harkess, Ronald and Parker, Richard A and Keerie, Catriona and Weir, Christopher J and Mills, Nicholas L and Investigators, on behalf of the HIGH-STEACS and Mills, Nicholas L and Strachan, Fiona E and Tuck, Christopher and Shah, Anoop S V and Anand, Atul and Ferry, Amy V and Lee, Kuan Ken and Chapman, Andrew R and Sandeman, Dennis and Adamson, Philip D and Stables, Catherine L and Marshall, Lucy and Stewart, Stacey D and Fujisawa, Takeshi and Vallejos, Catalina A and Tsanas, Athanasios and Hautvast, Mischa and McPherson, Jean and McKinlay, Lynn and Newby, David E and Fox, Keith A A and Berry, Colin and Walker, Simon and Weir, Christopher J and Ford, Ian and Gray, Alasdair and Collinson, Paul O and Apple, Fred S and Reid, Alan and Cruikshank, Anne and Findlay, Iain and Amoils, Shannon and McAllister, David A and Maguire, Donogh and Stevens, Jennifer and Norrie, John and Andrews, Jack P M and Adamson, Philip D and Moss, Alastair and Anwar, Mohamed S and Hung, John and Malo, Jonathan and Fischbacher, Colin M and Croal, Bernard L and Leslie, Stephen J and Keerie, Catriona and Parker, Richard A and Walker, Allan and Harkess, Ronnie and Wackett, Tony and Armstrong, Roma and Flood, Marion and Stirling, Laura and MacDonald, Claire and Sadat, Imran and Finlay, Frank and Charles, Heather and Linksted, Pamela and Young, Stephen and Alexander, Bill and Duncan, Chris},\\n doi = {10.1016/s0140-6736(18)31923-8},\\n journal = {The Lancet},\\n number = {10151}\\n}\",\"author_short\":[\"Shah,  A., S.\",\"Anand,  A.\",\"Strachan,  F., E.\",\"Ferry,  A., V.\",\"Lee,  K., K.\",\"Chapman,  A., R.\",\"Sandeman,  D.\",\"Stables,  C., L.\",\"Adamson,  P., D.\",\"Andrews,  J., P., M.\",\"Anwar,  M., S.\",\"Hung,  J.\",\"Moss,  A., J.\",\"O'Brien,  R.\",\"Berry,  C.\",\"Findlay,  I.\",\"Walker,  S.\",\"Cruickshank,  A.\",\"Reid,  A.\",\"Gray,  A.\",\"Collinson,  P., O.\",\"Apple,  F., S.\",\"McAllister,  D., A.\",\"Maguire,  D.\",\"Fox,  K., A., A.\",\"Newby,  D., E.\",\"Tuck,  C.\",\"Harkess,  R.\",\"Parker,  R., A.\",\"Keerie,  C.\",\"Weir,  C., J.\",\"Mills,  N., L.\",\"Investigators,  o., b., o., t., H.\",\"Mills,  N., L.\",\"Strachan,  F., E.\",\"Tuck,  C.\",\"Shah,  A., S., V.\",\"Anand,  A.\",\"Ferry,  A., V.\",\"Lee,  K., K.\",\"Chapman,  A., R.\",\"Sandeman,  D.\",\"Adamson,  P., D.\",\"Stables,  C., L.\",\"Marshall,  L.\",\"Stewart,  S., D.\",\"Fujisawa,  T.\",\"Vallejos,  C., A.\",\"Tsanas,  A.\",\"Hautvast,  M.\",\"McPherson,  J.\",\"McKinlay,  L.\",\"Newby,  D., E.\",\"Fox,  K., A., A.\",\"Berry,  C.\",\"Walker,  S.\",\"Weir,  C., J.\",\"Ford,  I.\",\"Gray,  A.\",\"Collinson,  P., O.\",\"Apple,  F., S.\",\"Reid,  A.\",\"Cruikshank,  A.\",\"Findlay,  I.\",\"Amoils,  S.\",\"McAllister,  D., A.\",\"Maguire,  D.\",\"Stevens,  J.\",\"Norrie,  J.\",\"Andrews,  J., P., M.\",\"Adamson,  P., D.\",\"Moss,  A.\",\"Anwar,  M., S.\",\"Hung,  J.\",\"Malo,  J.\",\"Fischbacher,  C., M.\",\"Croal,  B., L.\",\"Leslie,  S., J.\",\"Keerie,  C.\",\"Parker,  R., A.\",\"Walker,  A.\",\"Harkess,  R.\",\"Wackett,  T.\",\"Armstrong,  R.\",\"Flood,  M.\",\"Stirling,  L.\",\"MacDonald,  C.\",\"Sadat,  I.\",\"Finlay,  F.\",\"Charles,  H.\",\"Linksted,  P.\",\"Young,  S.\",\"Alexander,  B.\",\"Duncan,  C.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"shah-anand-strachan-ferry-lee-chapman-sandeman-stables-etal-highsensitivitytroponinintheevaluationofpatientswithsuspectedacutecoronarysyndromeasteppedwedgeclusterrandomisedcontrolledtrial-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Euclidean Distances as measures of speaker similarity including identical twin pairs: A forensic investigation using source and filter voice characteristics\",\"type\":\"article\",\"year\":\"2017\",\"keywords\":\"Acoustic analysis,Forensic phonetics,Pause fillers,Perceptual assessment,Twins,Voice quality\",\"pages\":\"25-38\",\"volume\":\"270\",\"id\":\"51c365ff-57d8-3763-8992-b56a9cc2ecd1\",\"created\":\"2019-02-22T11:03:04.627Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2019-02-22T11:03:04.627Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"There is a growing consensus that hybrid approaches are necessary for successful speaker characterization in Forensic Speaker Comparison (FSC); hence this study explores the forensic potential of voice features combining source and filter characteristics. The former relate to the action of the vocal folds while the latter reflect the geometry of the speaker's vocal tract. This set of features have been extracted from pause fillers, which are long enough for robust feature estimation while spontaneous enough to be extracted from voice samples in real forensic casework. Speaker similarity was measured using standardized Euclidean Distances (ED) between pairs of speakers: 54 different-speaker (DS) comparisons, 54 same-speaker (SS) comparisons and 12 comparisons between monozygotic twins (MZ). Results revealed that the differences between DS and SS comparisons were significant in both high quality and telephone-filtered recordings, with no false rejections and limited false acceptances; this finding suggests that this set of voice features is highly speaker-dependent and therefore forensically useful. Mean ED for MZ pairs lies between the average ED for SS comparisons and DS comparisons, as expected according to the literature on twin voices. Specific cases of MZ speakers with very high ED (i.e. strong dissimilarity) are discussed in the context of sociophonetic and twin studies. A preliminary simplification of the Vocal Profile Analysis (VPA) Scheme is proposed, which enables the quantification of voice quality features in the perceptual assessment of speaker similarity, and allows for the calculation of perceptual–acoustic correlations. The adequacy of z-score normalization for this study is also discussed, as well as the relevance of heat maps for detecting the so-called phantoms in recent approaches to the biometric menagerie.\",\"bibtype\":\"article\",\"author\":\"San Segundo, Eugenia and Tsanas, Athanasios and Gómez-Vilda, Pedro\",\"doi\":\"10.1016/j.forsciint.2016.11.020\",\"journal\":\"Forensic Science International\",\"bibtex\":\"@article{\\n title = {Euclidean Distances as measures of speaker similarity including identical twin pairs: A forensic investigation using source and filter voice characteristics},\\n type = {article},\\n year = {2017},\\n keywords = {Acoustic analysis,Forensic phonetics,Pause fillers,Perceptual assessment,Twins,Voice quality},\\n pages = {25-38},\\n volume = {270},\\n id = {51c365ff-57d8-3763-8992-b56a9cc2ecd1},\\n created = {2019-02-22T11:03:04.627Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2019-02-22T11:03:04.627Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {There is a growing consensus that hybrid approaches are necessary for successful speaker characterization in Forensic Speaker Comparison (FSC); hence this study explores the forensic potential of voice features combining source and filter characteristics. The former relate to the action of the vocal folds while the latter reflect the geometry of the speaker's vocal tract. This set of features have been extracted from pause fillers, which are long enough for robust feature estimation while spontaneous enough to be extracted from voice samples in real forensic casework. Speaker similarity was measured using standardized Euclidean Distances (ED) between pairs of speakers: 54 different-speaker (DS) comparisons, 54 same-speaker (SS) comparisons and 12 comparisons between monozygotic twins (MZ). Results revealed that the differences between DS and SS comparisons were significant in both high quality and telephone-filtered recordings, with no false rejections and limited false acceptances; this finding suggests that this set of voice features is highly speaker-dependent and therefore forensically useful. Mean ED for MZ pairs lies between the average ED for SS comparisons and DS comparisons, as expected according to the literature on twin voices. Specific cases of MZ speakers with very high ED (i.e. strong dissimilarity) are discussed in the context of sociophonetic and twin studies. A preliminary simplification of the Vocal Profile Analysis (VPA) Scheme is proposed, which enables the quantification of voice quality features in the perceptual assessment of speaker similarity, and allows for the calculation of perceptual–acoustic correlations. The adequacy of z-score normalization for this study is also discussed, as well as the relevance of heat maps for detecting the so-called phantoms in recent approaches to the biometric menagerie.},\\n bibtype = {article},\\n author = {San Segundo, Eugenia and Tsanas, Athanasios and Gómez-Vilda, Pedro},\\n doi = {10.1016/j.forsciint.2016.11.020},\\n journal = {Forensic Science International}\\n}\",\"author_short\":[\"San Segundo,  E.\",\"Tsanas,  A.\",\"Gómez-Vilda,  P.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"sansegundo-tsanas-gmezvilda-euclideandistancesasmeasuresofspeakersimilarityincludingidenticaltwinpairsaforensicinvestigationusingsourceandfiltervoicecharacteristics-2017\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Acoustic analysis\",\"Forensic phonetics\",\"Pause fillers\",\"Perceptual assessment\",\"Twins\",\"Voice quality\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Exploring Pause Fillers in Conversational Speech for Forensic Phonetics: Findings in a Spanish Cohort Including Twins\",\"type\":\"inproceedings\",\"year\":\"2017\",\"keywords\":\"contour,forensic phonetics,fundamental frequency,pause fillers,speech signal processing\",\"issue\":\"July\",\"id\":\"65e7e85a-6877-30ef-991d-018a9bef9339\",\"created\":\"2019-02-22T11:03:04.640Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2019-02-22T11:03:04.640Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Tsanas, A. and San Segundo, E. and Gómez-Vilda, P.\",\"doi\":\"10.1049/cp.2017.0161\",\"booktitle\":\"8th International Conference of Pattern Recognition Systems\",\"bibtex\":\"@inproceedings{\\n title = {Exploring Pause Fillers in Conversational Speech for Forensic Phonetics: Findings in a Spanish Cohort Including Twins},\\n type = {inproceedings},\\n year = {2017},\\n keywords = {contour,forensic phonetics,fundamental frequency,pause fillers,speech signal processing},\\n issue = {July},\\n id = {65e7e85a-6877-30ef-991d-018a9bef9339},\\n created = {2019-02-22T11:03:04.640Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2019-02-22T11:03:04.640Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Tsanas, A. and San Segundo, E. and Gómez-Vilda, P.},\\n doi = {10.1049/cp.2017.0161},\\n booktitle = {8th International Conference of Pattern Recognition Systems}\\n}\",\"author_short\":[\"Tsanas,  A.\",\"San Segundo,  E.\",\"Gómez-Vilda,  P.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tsanas-sansegundo-gmezvilda-exploringpausefillersinconversationalspeechforforensicphoneticsfindingsinaspanishcohortincludingtwins-2017\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"contour\",\"forensic phonetics\",\"fundamental frequency\",\"pause fillers\",\"speech signal processing\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Quantifying ultrasonic mouse vocalizations using acoustic analysis in a sueprvised statistical machine learning framework\",\"type\":\"article\",\"year\":\"2019\",\"pages\":\"8100\",\"volume\":\"9\",\"id\":\"80ec7b1e-c8a2-38d1-974d-98fc12780f15\",\"created\":\"2019-05-14T23:34:11.757Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2019-06-04T19:05:30.909Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Vogel, Adam and Tsanas, Athanasios and Scattoni, Maria Luisa\",\"journal\":\"Scientific Reports\",\"number\":\"1\",\"bibtex\":\"@article{\\n title = {Quantifying ultrasonic mouse vocalizations using acoustic analysis in a sueprvised statistical machine learning framework},\\n type = {article},\\n year = {2019},\\n pages = {8100},\\n volume = {9},\\n id = {80ec7b1e-c8a2-38d1-974d-98fc12780f15},\\n created = {2019-05-14T23:34:11.757Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2019-06-04T19:05:30.909Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Vogel, Adam and Tsanas, Athanasios and Scattoni, Maria Luisa},\\n journal = {Scientific Reports},\\n number = {1}\\n}\",\"author_short\":[\"Vogel,  A.\",\"Tsanas,  A.\",\"Scattoni,  M., L.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"vogel-tsanas-scattoni-quantifyingultrasonicmousevocalizationsusingacousticanalysisinasueprvisedstatisticalmachinelearningframework-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model\",\"type\":\"article\",\"year\":\"2019\",\"pages\":\"e028375\",\"volume\":\"9\",\"id\":\"4d28f322-204f-3f22-abf1-28fee9656249\",\"created\":\"2019-06-04T19:05:30.760Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-02-17T22:34:58.508Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Tibble, Holly and Tsanas, Athanasios and Horne, Elsie and Horne, Robert and Mizani, Mehrdad A and Simpson, Colin R and Sheikh, Aziz\",\"journal\":\"BMJ Open\",\"number\":\"7\",\"bibtex\":\"@article{\\n title = {Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model},\\n type = {article},\\n year = {2019},\\n pages = {e028375},\\n volume = {9},\\n id = {4d28f322-204f-3f22-abf1-28fee9656249},\\n created = {2019-06-04T19:05:30.760Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-02-17T22:34:58.508Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Tibble, Holly and Tsanas, Athanasios and Horne, Elsie and Horne, Robert and Mizani, Mehrdad A and Simpson, Colin R and Sheikh, Aziz},\\n journal = {BMJ Open},\\n number = {7}\\n}\",\"author_short\":[\"Tibble,  H.\",\"Tsanas,  A.\",\"Horne,  E.\",\"Horne,  R.\",\"Mizani,  M., A.\",\"Simpson,  C., R.\",\"Sheikh,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tibble-tsanas-horne-horne-mizani-simpson-sheikh-predictingasthmaattacksinprimarycareprotocolfordevelopingamachinelearningbasedpredictionmodel-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Machine Learning to Predict the Likelihood of Acute Myocardial Infarction\",\"type\":\"article\",\"year\":\"2019\",\"keywords\":\"acute coronary syndrome,infarction,machine learning,myocardial,see page 908,sources of funding,troponin\",\"pages\":\"899-909\",\"volume\":\"140\",\"websites\":\"http://www.ncbi.nlm.nih.gov/pubmed/31416346\",\"id\":\"7dfa3078-91b6-31de-8b9d-12fe7af21d1d\",\"created\":\"2019-09-15T10:20:10.856Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-02-17T19:39:57.570Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"BACKGROUND Variations in cardiac troponin concentrations by age, sex and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. METHODS A machine learning algorithm (myocardial-ischemic-injury-index [MI3]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3,013 patients and tested on 7,998 patients with suspected myocardial infarction. MI3 uses gradient boosting to compute a value (0-100) reflecting an individual's likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value (NPV), specificity and positive predictive value (PPV) for that individual. Assessment was by calibration and area under the receiver-operating-characteristic curve (AUC). Secondary analysis evaluated example MI3 thresholds from the training set that identified patients as low-risk (99% sensitivity) and high-risk (75% PPV), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology (ESC) rule-out pathways. RESULTS Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI3 was well calibrated with a very high AUC of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI3 thresholds identifying low-risk and high-risk patients in the training set were 1.6 and 49.7 respectively. In the test set, MI3 values were <1.6 in 69.5% with a NPV of 99.7% (99.5%-99.8%) and sensitivity of 97.8% (96.7-98.7%), and were ≥49.7 in 10.6% with a PPV of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI3 performed better than the ESC 0/3-hour pathway (sensitivity 82.5% [74.5-88.8%], specificity 92.2% [90.7-93.5%]) and the 99th percentile at any time-point (sensitivity 89.6% [87.4-91.6%]), specificity 89.3% [88.6-90.0%]). CONCLUSIONS Using machine learning, MI3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low-risk and high-risk patients who may benefit from earlier clinical decisions. CLINICAL TRIAL REGISTRATION Unique Identifier: Australian New Zealand Clinical Trials Registry: ACTRN12616001441404. URL: https://www.anzctr.org.au.\",\"bibtype\":\"article\",\"author\":\"Than, Martin P and Pickering, John W and Sandoval, Yader and Shah, Anoop S V and Tsanas, Athanasios and Apple, Fred S and Blankenberg, Stefan and Cullen, Louise and Mueller, Christian and Neumann, Johannes T and Twerenbold, Raphael and Westermann, Dirk and Beshiri, Agim and Mills, Nicholas L and MI3 collaborative, undefined\",\"doi\":\"10.1161/CIRCULATIONAHA.119.041980\",\"journal\":\"Circulation\",\"bibtex\":\"@article{\\n title = {Machine Learning to Predict the Likelihood of Acute Myocardial Infarction},\\n type = {article},\\n year = {2019},\\n keywords = {acute coronary syndrome,infarction,machine learning,myocardial,see page 908,sources of funding,troponin},\\n pages = {899-909},\\n volume = {140},\\n websites = {http://www.ncbi.nlm.nih.gov/pubmed/31416346},\\n id = {7dfa3078-91b6-31de-8b9d-12fe7af21d1d},\\n created = {2019-09-15T10:20:10.856Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-02-17T19:39:57.570Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {BACKGROUND Variations in cardiac troponin concentrations by age, sex and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. METHODS A machine learning algorithm (myocardial-ischemic-injury-index [MI3]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3,013 patients and tested on 7,998 patients with suspected myocardial infarction. MI3 uses gradient boosting to compute a value (0-100) reflecting an individual's likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value (NPV), specificity and positive predictive value (PPV) for that individual. Assessment was by calibration and area under the receiver-operating-characteristic curve (AUC). Secondary analysis evaluated example MI3 thresholds from the training set that identified patients as low-risk (99% sensitivity) and high-risk (75% PPV), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology (ESC) rule-out pathways. RESULTS Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI3 was well calibrated with a very high AUC of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI3 thresholds identifying low-risk and high-risk patients in the training set were 1.6 and 49.7 respectively. In the test set, MI3 values were <1.6 in 69.5% with a NPV of 99.7% (99.5%-99.8%) and sensitivity of 97.8% (96.7-98.7%), and were ≥49.7 in 10.6% with a PPV of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI3 performed better than the ESC 0/3-hour pathway (sensitivity 82.5% [74.5-88.8%], specificity 92.2% [90.7-93.5%]) and the 99th percentile at any time-point (sensitivity 89.6% [87.4-91.6%]), specificity 89.3% [88.6-90.0%]). CONCLUSIONS Using machine learning, MI3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low-risk and high-risk patients who may benefit from earlier clinical decisions. CLINICAL TRIAL REGISTRATION Unique Identifier: Australian New Zealand Clinical Trials Registry: ACTRN12616001441404. URL: https://www.anzctr.org.au.},\\n bibtype = {article},\\n author = {Than, Martin P and Pickering, John W and Sandoval, Yader and Shah, Anoop S V and Tsanas, Athanasios and Apple, Fred S and Blankenberg, Stefan and Cullen, Louise and Mueller, Christian and Neumann, Johannes T and Twerenbold, Raphael and Westermann, Dirk and Beshiri, Agim and Mills, Nicholas L and MI3 collaborative, undefined},\\n doi = {10.1161/CIRCULATIONAHA.119.041980},\\n journal = {Circulation}\\n}\",\"author_short\":[\"Than,  M., P.\",\"Pickering,  J., W.\",\"Sandoval,  Y.\",\"Shah,  A., S., V.\",\"Tsanas,  A.\",\"Apple,  F., S.\",\"Blankenberg,  S.\",\"Cullen,  L.\",\"Mueller,  C.\",\"Neumann,  J., T.\",\"Twerenbold,  R.\",\"Westermann,  D.\",\"Beshiri,  A.\",\"Mills,  N., L.\",\"MI3 collaborative\"],\"urls\":{\"Website\":\"http://www.ncbi.nlm.nih.gov/pubmed/31416346\"},\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"than-pickering-sandoval-shah-tsanas-apple-blankenberg-cullen-etal-machinelearningtopredictthelikelihoodofacutemyocardialinfarction-2019\",\"role\":\"author\",\"keyword\":[\"acute coronary syndrome\",\"infarction\",\"machine learning\",\"myocardial\",\"see page 908\",\"sources of funding\",\"troponin\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Biomedical speech signal insights from a large scale cohort across seven countries: the Parkinson's voice initiative study\",\"type\":\"inproceedings\",\"year\":\"2019\",\"keywords\":\"parkinson,pd,pvi,s,s disease,speech signal processing,sustained vowel phonations,voice initiative\",\"pages\":\"45-48\",\"city\":\"Florence, Italy\",\"id\":\"8d91e9a7-2a45-3156-b622-40eb83ca9d5d\",\"created\":\"2019-09-15T10:36:53.386Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-02-12T10:49:19.428Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Tsanas, Athanasios and Arora, Siddharth\",\"booktitle\":\"11th International Workshop Models and Analysis of Vocal Emissiong for Biomedical Applications (MAVEBA)\",\"bibtex\":\"@inproceedings{\\n title = {Biomedical speech signal insights from a large scale cohort across seven countries: the Parkinson's voice initiative study},\\n type = {inproceedings},\\n year = {2019},\\n keywords = {parkinson,pd,pvi,s,s disease,speech signal processing,sustained vowel phonations,voice initiative},\\n pages = {45-48},\\n city = {Florence, Italy},\\n id = {8d91e9a7-2a45-3156-b622-40eb83ca9d5d},\\n created = {2019-09-15T10:36:53.386Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-02-12T10:49:19.428Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Tsanas, Athanasios and Arora, Siddharth},\\n booktitle = {11th International Workshop Models and Analysis of Vocal Emissiong for Biomedical Applications (MAVEBA)}\\n}\",\"author_short\":[\"Tsanas,  A.\",\"Arora,  S.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tsanas-arora-biomedicalspeechsignalinsightsfromalargescalecohortacrosssevencountriestheparkinsonsvoiceinitiativestudy-2019\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"parkinson\",\"pd\",\"pvi\",\"s\",\"s disease\",\"speech signal processing\",\"sustained vowel phonations\",\"voice initiative\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity\",\"type\":\"inproceedings\",\"year\":\"2019\",\"pages\":\"3412-3415\",\"city\":\"Berlin, Germany\",\"id\":\"71341c45-b5f6-3384-9cd9-d8ecc00e832d\",\"created\":\"2019-09-15T10:36:53.439Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-02-17T22:34:58.507Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Tsanas, Athanasios\",\"booktitle\":\"41st IEEE Engineering in Medicine and Biology Conference\",\"bibtex\":\"@inproceedings{\\n title = {New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity},\\n type = {inproceedings},\\n year = {2019},\\n pages = {3412-3415},\\n city = {Berlin, Germany},\\n id = {71341c45-b5f6-3384-9cd9-d8ecc00e832d},\\n created = {2019-09-15T10:36:53.439Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-02-17T22:34:58.507Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Tsanas, Athanasios},\\n booktitle = {41st IEEE Engineering in Medicine and Biology Conference}\\n}\",\"author_short\":[\"Tsanas,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tsanas-newinsightsintoparkinsonsdiseasethroughstatisticalanalysisofstandardclinicalscalesquantifyingsymptomseverity-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2019\",\"keywords\":\"Characterizing stress,Cooperative framework,Data acquisition,Emotional stress,Stress behavior in human-computer interaction\",\"pages\":\"149-160\",\"volume\":\"1148\",\"id\":\"e935baeb-8205-3129-9e87-61515e9542e8\",\"created\":\"2019-09-15T11:14:59.627Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2019-09-15T11:21:21.359Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inbook\",\"author\":\"Palacios-Alonso, Daniel and Lázaro-Carrascosa, Carlos and López-Arribas, Agustín and Meléndez-Morales, Guillermo and Gómez-Rodellar, Andrés and Loro-Álavez, Andrés and Nieto-Lluis, Victor and Rodellar-Biarge, Victoria and Tsanas, Athanasios and Gómez-Vilda, Pedro\",\"editor\":\"Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H.\",\"doi\":\"10.1007/978-3-030-19591-5_16\",\"chapter\":\"Assessing an application of spontaneous stressed speech - emotions portal\",\"title\":\"Understanding the Brain Function and Emotions. IWINAC 2019 (Lecture Notes in Computer Science series)\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2019},\\n keywords = {Characterizing stress,Cooperative framework,Data acquisition,Emotional stress,Stress behavior in human-computer interaction},\\n pages = {149-160},\\n volume = {1148},\\n id = {e935baeb-8205-3129-9e87-61515e9542e8},\\n created = {2019-09-15T11:14:59.627Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2019-09-15T11:21:21.359Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inbook},\\n author = {Palacios-Alonso, Daniel and Lázaro-Carrascosa, Carlos and López-Arribas, Agustín and Meléndez-Morales, Guillermo and Gómez-Rodellar, Andrés and Loro-Álavez, Andrés and Nieto-Lluis, Victor and Rodellar-Biarge, Victoria and Tsanas, Athanasios and Gómez-Vilda, Pedro},\\n editor = {Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H.},\\n doi = {10.1007/978-3-030-19591-5_16},\\n chapter = {Assessing an application of spontaneous stressed speech - emotions portal},\\n title = {Understanding the Brain Function and Emotions. IWINAC 2019 (Lecture Notes in Computer Science series)}\\n}\",\"author_short\":[\"Palacios-Alonso,  D.\",\"Lázaro-Carrascosa,  C.\",\"López-Arribas,  A.\",\"Meléndez-Morales,  G.\",\"Gómez-Rodellar,  A.\",\"Loro-Álavez,  A.\",\"Nieto-Lluis,  V.\",\"Rodellar-Biarge,  V.\",\"Tsanas,  A.\",\"Gómez-Vilda,  P.\"],\"editor_short\":[\"Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J.,  A., H.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"palaciosalonso-lzarocarrascosa-lpezarribas-melndezmorales-gmezrodellar-lorolavez-nietolluis-rodellarbiarge-etal-understandingthebrainfunctionandemotionsiwinac2019lecturenotesincomputerscienceseries-2019\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Characterizing stress\",\"Cooperative framework\",\"Data acquisition\",\"Emotional stress\",\"Stress behavior in human-computer interaction\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Exploring telephone-quality speech signals towards parkinson's disease assessment in a large acoustically non-controlled study\",\"type\":\"inproceedings\",\"year\":\"2019\",\"keywords\":\"Data visualization,Dimensionality reduction,Parkinson's Disease (PD),Sustained vowels\",\"pages\":\"953-956\",\"id\":\"efdb297f-c825-3b67-8897-abc9510fc296\",\"created\":\"2020-02-12T10:49:19.200Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-05-08T17:19:02.659Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"The use of high-quality speech signals has led to considerable breakthroughs in Parkinson's Disease (PD) in the last decade. These include accurate differentiation of PD versus Healthy Controls (HC) and monitoring longitudinal PD symptom severity. We recently concluded the Parkinson's Voice Initiative (PVI) study collecting data from a very large cohort under non-controlled acoustic conditions. We acoustically characterized 11,942 recordings from 6531 US-based participants using 307 dysphonia measures. We selected a robust subset of 30 dysphonia measures using Gram-Schmidt Orthogonalization (GSO). We projected the data onto a two-dimensional representation using t-distributed stochastic neighbor embedding to facilitate visual exploration, and used hierarchical clustering to understand data homogeneity. We demonstrate that there is considerable overlap in the projected feature space between PD and HC, making the binary classification task particularly challenging. The data was grouped into nine clusters using hierarchical clustering which was in broad agreement with the projected two-dimensional representation. These results provide some new insights into understanding the new challenges posed in the PVI project where acoustic recordings conditions were not controlled.\",\"bibtype\":\"inproceedings\",\"author\":\"Tsanas, Athanasios and Arora, Siddharth\",\"doi\":\"10.1109/BIBE.2019.00178\",\"booktitle\":\"19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)\",\"bibtex\":\"@inproceedings{\\n title = {Exploring telephone-quality speech signals towards parkinson's disease assessment in a large acoustically non-controlled study},\\n type = {inproceedings},\\n year = {2019},\\n keywords = {Data visualization,Dimensionality reduction,Parkinson's Disease (PD),Sustained vowels},\\n pages = {953-956},\\n id = {efdb297f-c825-3b67-8897-abc9510fc296},\\n created = {2020-02-12T10:49:19.200Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-05-08T17:19:02.659Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The use of high-quality speech signals has led to considerable breakthroughs in Parkinson's Disease (PD) in the last decade. These include accurate differentiation of PD versus Healthy Controls (HC) and monitoring longitudinal PD symptom severity. We recently concluded the Parkinson's Voice Initiative (PVI) study collecting data from a very large cohort under non-controlled acoustic conditions. We acoustically characterized 11,942 recordings from 6531 US-based participants using 307 dysphonia measures. We selected a robust subset of 30 dysphonia measures using Gram-Schmidt Orthogonalization (GSO). We projected the data onto a two-dimensional representation using t-distributed stochastic neighbor embedding to facilitate visual exploration, and used hierarchical clustering to understand data homogeneity. We demonstrate that there is considerable overlap in the projected feature space between PD and HC, making the binary classification task particularly challenging. The data was grouped into nine clusters using hierarchical clustering which was in broad agreement with the projected two-dimensional representation. These results provide some new insights into understanding the new challenges posed in the PVI project where acoustic recordings conditions were not controlled.},\\n bibtype = {inproceedings},\\n author = {Tsanas, Athanasios and Arora, Siddharth},\\n doi = {10.1109/BIBE.2019.00178},\\n booktitle = {19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)}\\n}\",\"author_short\":[\"Tsanas,  A.\",\"Arora,  S.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tsanas-arora-exploringtelephonequalityspeechsignalstowardsparkinsonsdiseaseassessmentinalargeacousticallynoncontrolledstudy-2019\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Data visualization\",\"Dimensionality reduction\",\"Parkinson's Disease (PD)\",\"Sustained vowels\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Challenges of clustering multimodal clinical data: a review of applications in asthma subtyping\",\"type\":\"article\",\"year\":\"2020\",\"pages\":\"e16452\",\"volume\":\"8\",\"websites\":\"https://medinform.jmir.org/2020/5/e16452/\",\"id\":\"86649f63-6182-3fb3-92be-e820cb4f3238\",\"created\":\"2020-02-12T10:49:19.267Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-05-28T13:50:36.828Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Horne, Elsie and Tibble, Holly and Sheikh, Aziz and Tsanas, Athanasios\",\"doi\":\"10.2196/16452\",\"journal\":\"JMIR Medical Informatics\",\"number\":\"5\",\"bibtex\":\"@article{\\n title = {Challenges of clustering multimodal clinical data: a review of applications in asthma subtyping},\\n type = {article},\\n year = {2020},\\n pages = {e16452},\\n volume = {8},\\n websites = {https://medinform.jmir.org/2020/5/e16452/},\\n id = {86649f63-6182-3fb3-92be-e820cb4f3238},\\n created = {2020-02-12T10:49:19.267Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-05-28T13:50:36.828Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Horne, Elsie and Tibble, Holly and Sheikh, Aziz and Tsanas, Athanasios},\\n doi = {10.2196/16452},\\n journal = {JMIR Medical Informatics},\\n number = {5}\\n}\",\"author_short\":[\"Horne,  E.\",\"Tibble,  H.\",\"Sheikh,  A.\",\"Tsanas,  A.\"],\"urls\":{\"Website\":\"https://medinform.jmir.org/2020/5/e16452/\"},\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"horne-tibble-sheikh-tsanas-challengesofclusteringmultimodalclinicaldataareviewofapplicationsinasthmasubtyping-2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Investigating motility and pattern formation in pluripotent stem cells through agent-based modeling\",\"type\":\"inproceedings\",\"year\":\"2019\",\"keywords\":\"Agent-based modelling,Pattern formation,Pluripotent stem cells\",\"pages\":\"909-913\",\"publisher\":\"IEEE\",\"city\":\"Athens, Greece\",\"id\":\"1451e661-713b-37e7-8658-a48ec10384d1\",\"created\":\"2020-02-17T22:00:12.274Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-05-08T17:19:02.721Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Understanding and predicting the pattern formation in groups of pluripotent stem cells has the potential to improve efficiency and efficacy of stem cell therapies. However, the underlying molecular mechanisms of pluripotent stem cell behaviors are highly complex and are currently still not fully understood. A key practical question is whether deep biological modelling of the cells is essential to predict their pattern formation, or whether there is sufficient predictive power in simply modelling their behaviors and interactions at a higher level. This study focuses on the social interactions and behaviors of pluripotent stem cells at a high-level to predict aggregate crowd behaviors within a level of uncertainty. Agent-based modelling was applied to study the pattern formation in pluripotent stem cells. Five models were established to test four biologically plausible rules of cell motility in terms of: a) velocity, b) directional persistence time, c) directional movements, and d) border effect. We found that it is possible that cells' directional movements based on local density play an important role of the pattern formation, and pattern formation in pluripotent stem cells is governed by a complex combination of rules in our agent-based model simulations, which account for much of the variability observed in experimental findings.\",\"bibtype\":\"inproceedings\",\"author\":\"Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave\",\"doi\":\"10.1109/BIBE.2019.00170\",\"booktitle\":\"19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)\",\"bibtex\":\"@inproceedings{\\n title = {Investigating motility and pattern formation in pluripotent stem cells through agent-based modeling},\\n type = {inproceedings},\\n year = {2019},\\n keywords = {Agent-based modelling,Pattern formation,Pluripotent stem cells},\\n pages = {909-913},\\n publisher = {IEEE},\\n city = {Athens, Greece},\\n id = {1451e661-713b-37e7-8658-a48ec10384d1},\\n created = {2020-02-17T22:00:12.274Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-05-08T17:19:02.721Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Understanding and predicting the pattern formation in groups of pluripotent stem cells has the potential to improve efficiency and efficacy of stem cell therapies. However, the underlying molecular mechanisms of pluripotent stem cell behaviors are highly complex and are currently still not fully understood. A key practical question is whether deep biological modelling of the cells is essential to predict their pattern formation, or whether there is sufficient predictive power in simply modelling their behaviors and interactions at a higher level. This study focuses on the social interactions and behaviors of pluripotent stem cells at a high-level to predict aggregate crowd behaviors within a level of uncertainty. Agent-based modelling was applied to study the pattern formation in pluripotent stem cells. Five models were established to test four biologically plausible rules of cell motility in terms of: a) velocity, b) directional persistence time, c) directional movements, and d) border effect. We found that it is possible that cells' directional movements based on local density play an important role of the pattern formation, and pattern formation in pluripotent stem cells is governed by a complex combination of rules in our agent-based model simulations, which account for much of the variability observed in experimental findings.},\\n bibtype = {inproceedings},\\n author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},\\n doi = {10.1109/BIBE.2019.00170},\\n booktitle = {19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)}\\n}\",\"author_short\":[\"Wang,  M.\",\"Tsanas,  A.\",\"Blin,  G.\",\"Robertson,  D.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"wang-tsanas-blin-robertson-investigatingmotilityandpatternformationinpluripotentstemcellsthroughagentbasedmodeling-2019\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Agent-based modelling\",\"Pattern formation\",\"Pluripotent stem cells\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Assessing Preferred Proximity Between Different Types of Embryonic Stem Cells\",\"type\":\"inproceedings\",\"year\":\"2020\",\"pages\":\"377-381\",\"city\":\"Valetta, Malta\",\"id\":\"167c26a9-b52c-3181-876a-b72be418ce22\",\"created\":\"2020-02-17T22:00:12.342Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-03-26T14:29:42.035Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave\",\"booktitle\":\"13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)\",\"bibtex\":\"@inproceedings{\\n title = {Assessing Preferred Proximity Between Different Types of Embryonic Stem Cells},\\n type = {inproceedings},\\n year = {2020},\\n pages = {377-381},\\n city = {Valetta, Malta},\\n id = {167c26a9-b52c-3181-876a-b72be418ce22},\\n created = {2020-02-17T22:00:12.342Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-03-26T14:29:42.035Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},\\n booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\\n}\",\"author_short\":[\"Wang,  M.\",\"Tsanas,  A.\",\"Blin,  G.\",\"Robertson,  D.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"wang-tsanas-blin-robertson-assessingpreferredproximitybetweendifferenttypesofembryonicstemcells-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Heterogeneity in asthma medication adherence measurement\",\"type\":\"inproceedings\",\"year\":\"2019\",\"keywords\":\"Adherence,Asthma,Electronic monitoring,Medication,Pediatric\",\"pages\":\"899-903\",\"city\":\"Athens, Greece\",\"id\":\"3b61ab79-336d-3a0d-98a4-b972e1204f95\",\"created\":\"2020-02-17T22:00:12.347Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-05-08T17:19:02.655Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Medication non-adherence is strongly associated with poor asthma control and outcomes. Many studies use an aggregate measure of adherence, such as the percentage of prescribed doses that were taken, however this conceals variation between patients' medication-taking routines. Electronic monitoring devices, which precisely record the date and time of a dose being actuated from an inhaler, provide the means to objectively and remotely monitor adherence behavior patterns. This secondary analysis of a New Zealand audio-visual medication reminder intervention study visually explored the relationships, variation, and heterogeneity between multiple measures of adherence, in 211 children aged 6-15 years old who presented to an emergency department with an asthma attack. Our findings highlight the weakness of statistical relationships between measures of adherence, and the irregularity in patient medication-taking behavior. This demonstrates that a single aggregate adherence measure fails to detect asthma patients for whom their day-to-day medication taking (implementation) is inconsistent with their longitudinal medication taking (persistence).\",\"bibtype\":\"inproceedings\",\"author\":\"Tibble, Holly and Chan, Amy and Mitchell, Edwin A. and Horne, Rob and Mizani, Mehrdad A. and Sheikh, Aziz and Tsanas, Athanasios\",\"doi\":\"10.1109/BIBE.2019.00168\",\"booktitle\":\"19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)\",\"bibtex\":\"@inproceedings{\\n title = {Heterogeneity in asthma medication adherence measurement},\\n type = {inproceedings},\\n year = {2019},\\n keywords = {Adherence,Asthma,Electronic monitoring,Medication,Pediatric},\\n pages = {899-903},\\n city = {Athens, Greece},\\n id = {3b61ab79-336d-3a0d-98a4-b972e1204f95},\\n created = {2020-02-17T22:00:12.347Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-05-08T17:19:02.655Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Medication non-adherence is strongly associated with poor asthma control and outcomes. Many studies use an aggregate measure of adherence, such as the percentage of prescribed doses that were taken, however this conceals variation between patients' medication-taking routines. Electronic monitoring devices, which precisely record the date and time of a dose being actuated from an inhaler, provide the means to objectively and remotely monitor adherence behavior patterns. This secondary analysis of a New Zealand audio-visual medication reminder intervention study visually explored the relationships, variation, and heterogeneity between multiple measures of adherence, in 211 children aged 6-15 years old who presented to an emergency department with an asthma attack. Our findings highlight the weakness of statistical relationships between measures of adherence, and the irregularity in patient medication-taking behavior. This demonstrates that a single aggregate adherence measure fails to detect asthma patients for whom their day-to-day medication taking (implementation) is inconsistent with their longitudinal medication taking (persistence).},\\n bibtype = {inproceedings},\\n author = {Tibble, Holly and Chan, Amy and Mitchell, Edwin A. and Horne, Rob and Mizani, Mehrdad A. and Sheikh, Aziz and Tsanas, Athanasios},\\n doi = {10.1109/BIBE.2019.00168},\\n booktitle = {19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)}\\n}\",\"author_short\":[\"Tibble,  H.\",\"Chan,  A.\",\"Mitchell,  E., A.\",\"Horne,  R.\",\"Mizani,  M., A.\",\"Sheikh,  A.\",\"Tsanas,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tibble-chan-mitchell-horne-mizani-sheikh-tsanas-heterogeneityinasthmamedicationadherencemeasurement-2019\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Adherence\",\"Asthma\",\"Electronic monitoring\",\"Medication\",\"Pediatric\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Data-driven Insights Towards Risk Assessment of Postpartum Depression\",\"type\":\"inproceedings\",\"year\":\"2020\",\"pages\":\"382-389\",\"city\":\"Valetta, Malta\",\"id\":\"655e8843-eb90-37d6-875b-48addf1d1ed6\",\"created\":\"2020-02-17T22:00:12.362Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-03-26T14:29:42.007Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Valavani, Evdoxia and Doudesis, Dimitrios and Kourtesis, Ioannis and Chin, Richard F.M. and MacIntyre, Donald J. and Fletcher-Watson, Sue and Boardman, James P and Tsanas, Athanasios\",\"booktitle\":\"13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)\",\"bibtex\":\"@inproceedings{\\n title = {Data-driven Insights Towards Risk Assessment of Postpartum Depression},\\n type = {inproceedings},\\n year = {2020},\\n pages = {382-389},\\n city = {Valetta, Malta},\\n id = {655e8843-eb90-37d6-875b-48addf1d1ed6},\\n created = {2020-02-17T22:00:12.362Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-03-26T14:29:42.007Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Valavani, Evdoxia and Doudesis, Dimitrios and Kourtesis, Ioannis and Chin, Richard F.M. and MacIntyre, Donald J. and Fletcher-Watson, Sue and Boardman, James P and Tsanas, Athanasios},\\n booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\\n}\",\"author_short\":[\"Valavani,  E.\",\"Doudesis,  D.\",\"Kourtesis,  I.\",\"Chin,  R., F.\",\"MacIntyre,  D., J.\",\"Fletcher-Watson,  S.\",\"Boardman,  J., P.\",\"Tsanas,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"valavani-doudesis-kourtesis-chin-macintyre-fletcherwatson-boardman-tsanas-datadriveninsightstowardsriskassessmentofpostpartumdepression-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Large-scale Clustering of People Diagnosed with Parkinson’s Disease using Acoustic Analysis of Sustained Vowels: Findings in the Parkinson’s Voice Initiative Study\",\"type\":\"inproceedings\",\"year\":\"2020\",\"pages\":\"369-376\",\"city\":\"Valetta, 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on Biomedical Systems and Technology (BIOSTEC)\",\"bibtex\":\"@inproceedings{\\n title = {Parkinson’s Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions},\\n type = {inproceedings},\\n year = {2020},\\n pages = {359-368},\\n city = {Valetta, Malta},\\n id = {fbdbeafc-f820-3905-9e4c-12bc8d6cde5d},\\n created = {2020-04-13T10:55:42.288Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-06-27T10:14:10.758Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Álvarez, Agustín and Gómez, Andrés and Palacios, Daniel and Mekyska, Jiri and Tsanas, Athanasios and Gómez, Pedro and Martínez, Rafael},\\n doi = {10.5220/0009189403590368},\\n booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\\n}\",\"author_short\":[\"Álvarez,  A.\",\"Gómez,  A.\",\"Palacios,  D.\",\"Mekyska,  J.\",\"Tsanas,  A.\",\"Gómez,  P.\",\"Martínez,  R.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"lvarez-gmez-palacios-mekyska-tsanas-gmez-martnez-parkinsonsdiseaseglottalflowcharacterizationphonationfeaturesvsamplitudedistributions-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Objective characterization of activity, sleep, and circadian rhythm patterns using a wrist-worn actigraphy sensor: insights into post-traumatic stress disorder\",\"type\":\"article\",\"year\":\"2020\",\"keywords\":\"actigraphy,geneactiv,posttraumatic stress disorder,sleep,wearable technology\",\"pages\":\"e14306\",\"volume\":\"8\",\"websites\":\"https://mhealth.jmir.org/2020/4/e14306/\",\"id\":\"361f4a71-40ae-3eff-8014-71ba3de15ba1\",\"created\":\"2020-04-30T12:11:42.405Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-04-30T12:11:42.405Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Tsanas, Athanasios and Woodward, Elizabeth and Ehlers, Anke\",\"doi\":\"10.2196/14306\",\"journal\":\"JMIR mHealth and uHealth\",\"number\":\"4\",\"bibtex\":\"@article{\\n title = {Objective characterization of activity, sleep, and circadian rhythm patterns using a wrist-worn actigraphy sensor: insights into post-traumatic stress disorder},\\n type = {article},\\n year = {2020},\\n keywords = {actigraphy,geneactiv,posttraumatic stress disorder,sleep,wearable technology},\\n pages = {e14306},\\n volume = {8},\\n websites = {https://mhealth.jmir.org/2020/4/e14306/},\\n id = {361f4a71-40ae-3eff-8014-71ba3de15ba1},\\n created = {2020-04-30T12:11:42.405Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-04-30T12:11:42.405Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Tsanas, Athanasios and Woodward, Elizabeth and Ehlers, Anke},\\n doi = {10.2196/14306},\\n journal = {JMIR mHealth and uHealth},\\n number = {4}\\n}\",\"author_short\":[\"Tsanas,  A.\",\"Woodward,  E.\",\"Ehlers,  A.\"],\"urls\":{\"Website\":\"https://mhealth.jmir.org/2020/4/e14306/\"},\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tsanas-woodward-ehlers-objectivecharacterizationofactivitysleepandcircadianrhythmpatternsusingawristwornactigraphysensorinsightsintoposttraumaticstressdisorder-2020\",\"role\":\"author\",\"keyword\":[\"actigraphy\",\"geneactiv\",\"posttraumatic stress disorder\",\"sleep\",\"wearable technology\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice\",\"type\":\"article\",\"year\":\"2019\",\"pages\":\"2871-2884\",\"volume\":\"145\",\"websites\":\"http://dx.doi.org/10.1121/1.5100272\",\"id\":\"c1bf6321-9df0-395d-a2e2-b489419875bd\",\"created\":\"2020-04-30T12:14:51.819Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-04-30T12:14:51.819Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.\",\"bibtype\":\"article\",\"author\":\"Arora, Siddharth and Baghai-Ravary, Ladan and Tsanas, Athanasios\",\"doi\":\"10.1121/1.5100272\",\"journal\":\"Journal of the Acoustical Society of America\",\"number\":\"5\",\"bibtex\":\"@article{\\n title = {Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice},\\n type = {article},\\n year = {2019},\\n pages = {2871-2884},\\n volume = {145},\\n websites = {http://dx.doi.org/10.1121/1.5100272},\\n id = {c1bf6321-9df0-395d-a2e2-b489419875bd},\\n created = {2020-04-30T12:14:51.819Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-04-30T12:14:51.819Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.},\\n bibtype = {article},\\n author = {Arora, Siddharth and Baghai-Ravary, Ladan and Tsanas, Athanasios},\\n doi = {10.1121/1.5100272},\\n journal = {Journal of the Acoustical Society of America},\\n number = {5}\\n}\",\"author_short\":[\"Arora,  S.\",\"Baghai-Ravary,  L.\",\"Tsanas,  A.\"],\"urls\":{\"Website\":\"http://dx.doi.org/10.1121/1.5100272\"},\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"arora-baghairavary-tsanas-developingalargescalepopulationscreeningtoolfortheassessmentofparkinsonsdiseaseusingtelephonequalityvoice-2019\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Applications of machine learning in real-life digital health interventions: Review of the literature\",\"type\":\"article\",\"year\":\"2019\",\"keywords\":\"Artificial intelligence,Data mining,Digital health,Machine learning,Review,Telemedicine\",\"pages\":\"1-9\",\"volume\":\"21\",\"id\":\"4addf79b-282a-34d9-9174-7f7a07d3686d\",\"created\":\"2020-04-30T12:14:51.821Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-04-30T12:14:51.821Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Background: Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals. Objective: Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain. Methods: We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction). Results: Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes. Conclusions: This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.\",\"bibtype\":\"article\",\"author\":\"Triantafyllidis, Andreas K. and Tsanas, Athanasios\",\"doi\":\"10.2196/12286\",\"journal\":\"Journal of Medical Internet Research\",\"number\":\"4\",\"bibtex\":\"@article{\\n title = {Applications of machine learning in real-life digital health interventions: Review of the literature},\\n type = {article},\\n year = {2019},\\n keywords = {Artificial intelligence,Data mining,Digital health,Machine learning,Review,Telemedicine},\\n pages = {1-9},\\n volume = {21},\\n id = {4addf79b-282a-34d9-9174-7f7a07d3686d},\\n created = {2020-04-30T12:14:51.821Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-04-30T12:14:51.821Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Background: Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals. Objective: Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain. Methods: We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction). Results: Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes. Conclusions: This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.},\\n bibtype = {article},\\n author = {Triantafyllidis, Andreas K. and Tsanas, Athanasios},\\n doi = {10.2196/12286},\\n journal = {Journal of Medical Internet Research},\\n number = {4}\\n}\",\"author_short\":[\"Triantafyllidis,  A., K.\",\"Tsanas,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"triantafyllidis-tsanas-applicationsofmachinelearninginreallifedigitalhealthinterventionsreviewoftheliterature-2019\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Artificial intelligence\",\"Data mining\",\"Digital health\",\"Machine learning\",\"Review\",\"Telemedicine\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Telemedicine Cognitive Behavioural Therapy for Anxiety after Stroke: Proof-of-Concept Randomized Controlled Trial\",\"type\":\"article\",\"year\":\"2020\",\"pages\":\"2297-2306\",\"volume\":\"51\",\"id\":\"5e8fd247-1354-38c5-bc39-02238bec766a\",\"created\":\"2020-05-08T17:12:49.455Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-08-22T12:49:11.720Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Chun, H-Y. Yvonne and Carson, Alan J and Tsanas, Athanasios and Dennis, Martin S and Mead, Gillian E and Whiteley, William N\",\"journal\":\"Stroke\",\"bibtex\":\"@article{\\n title = {Telemedicine Cognitive Behavioural Therapy for Anxiety after Stroke: Proof-of-Concept Randomized Controlled Trial},\\n type = {article},\\n year = {2020},\\n pages = {2297-2306},\\n volume = {51},\\n id = {5e8fd247-1354-38c5-bc39-02238bec766a},\\n created = {2020-05-08T17:12:49.455Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-08-22T12:49:11.720Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Chun, H-Y. Yvonne and Carson, Alan J and Tsanas, Athanasios and Dennis, Martin S and Mead, Gillian E and Whiteley, William N},\\n journal = {Stroke}\\n}\",\"author_short\":[\"Chun,  H., Y.\",\"Carson,  A., J.\",\"Tsanas,  A.\",\"Dennis,  M., S.\",\"Mead,  G., E.\",\"Whiteley,  W., N.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"chun-carson-tsanas-dennis-mead-whiteley-telemedicinecognitivebehaviouraltherapyforanxietyafterstrokeproofofconceptrandomizedcontrolledtrial-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications\",\"type\":\"article\",\"year\":\"2020\",\"pages\":\"237-270\",\"volume\":\"410\",\"websites\":\"https://www.sciencedirect.com/science/article/abs/pii/S0925231220309292?via%3Dihub\",\"id\":\"48a29a5b-f09b-3ffc-ab3f-17558dc46ef8\",\"created\":\"2020-05-27T09:42:19.096Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-08-25T16:21:03.340Z\",\"read\":\"true\",\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Gorriz, Juan M and Ramirez, Javier and Ortiz, Andres and Martinez-Murcia, Francisco J and Segovia, Fermin and Suckling, John and Leming, Matthew and Zhang, Yu-Dong and Alvarez-Sanchez, Jose Ramon and Bologna, Guido and Bonomini, Paula and Casado, Fernando E and Charte, David and Charte, Francisco and Contreras, Ricardo and Cuesta-Infante, Alfredo and Duro, Richard J and Fernandez-Caballero, Antonio and Fernandez-Jover, Eduardo and Gomez-Vilda, Pedro and Grana, Manuel and Herrera, Francisco and Iglesias, Roberto and Lekova, Anna and de Lope, Javier and Lopez-Rubio, Ezequiel and Martinez Tomas, Rafael and Molina-Cabello, Miguel A and Montemayor, Antonio S and Novais, Paulo and Palacios-Alonso, Daniel and Pantrigo, Juaj J and Payne, Bryson R and de la Paz Lopez, Felix and Angelica Pinninghoff, Maria and Rincon, Mariano and Sanstos, Jose and Thurnhofer-Hemsi, Karl and Tsanas, Athanasios and Varela, Ramiro and Ferrandez, Jose M\",\"journal\":\"Neurocomputing\",\"bibtex\":\"@article{\\n title = {Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications},\\n type = {article},\\n year = {2020},\\n pages = {237-270},\\n volume = {410},\\n websites = {https://www.sciencedirect.com/science/article/abs/pii/S0925231220309292?via%3Dihub},\\n id = {48a29a5b-f09b-3ffc-ab3f-17558dc46ef8},\\n created = {2020-05-27T09:42:19.096Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-08-25T16:21:03.340Z},\\n read = {true},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Gorriz, Juan M and Ramirez, Javier and Ortiz, Andres and Martinez-Murcia, Francisco J and Segovia, Fermin and Suckling, John and Leming, Matthew and Zhang, Yu-Dong and Alvarez-Sanchez, Jose Ramon and Bologna, Guido and Bonomini, Paula and Casado, Fernando E and Charte, David and Charte, Francisco and Contreras, Ricardo and Cuesta-Infante, Alfredo and Duro, Richard J and Fernandez-Caballero, Antonio and Fernandez-Jover, Eduardo and Gomez-Vilda, Pedro and Grana, Manuel and Herrera, Francisco and Iglesias, Roberto and Lekova, Anna and de Lope, Javier and Lopez-Rubio, Ezequiel and Martinez Tomas, Rafael and Molina-Cabello, Miguel A and Montemayor, Antonio S and Novais, Paulo and Palacios-Alonso, Daniel and Pantrigo, Juaj J and Payne, Bryson R and de la Paz Lopez, Felix and Angelica Pinninghoff, Maria and Rincon, Mariano and Sanstos, Jose and Thurnhofer-Hemsi, Karl and Tsanas, Athanasios and Varela, Ramiro and Ferrandez, Jose M},\\n journal = {Neurocomputing}\\n}\",\"author_short\":[\"Gorriz,  J., M.\",\"Ramirez,  J.\",\"Ortiz,  A.\",\"Martinez-Murcia,  F., J.\",\"Segovia,  F.\",\"Suckling,  J.\",\"Leming,  M.\",\"Zhang,  Y.\",\"Alvarez-Sanchez,  J., R.\",\"Bologna,  G.\",\"Bonomini,  P.\",\"Casado,  F., E.\",\"Charte,  D.\",\"Charte,  F.\",\"Contreras,  R.\",\"Cuesta-Infante,  A.\",\"Duro,  R., J.\",\"Fernandez-Caballero,  A.\",\"Fernandez-Jover,  E.\",\"Gomez-Vilda,  P.\",\"Grana,  M.\",\"Herrera,  F.\",\"Iglesias,  R.\",\"Lekova,  A.\",\"de Lope,  J.\",\"Lopez-Rubio,  E.\",\"Martinez Tomas,  R.\",\"Molina-Cabello,  M., A.\",\"Montemayor,  A., S.\",\"Novais,  P.\",\"Palacios-Alonso,  D.\",\"Pantrigo,  J., J.\",\"Payne,  B., R.\",\"de la Paz Lopez,  F.\",\"Angelica Pinninghoff,  M.\",\"Rincon,  M.\",\"Sanstos,  J.\",\"Thurnhofer-Hemsi,  K.\",\"Tsanas,  A.\",\"Varela,  R.\",\"Ferrandez,  J., M.\"],\"urls\":{\"Website\":\"https://www.sciencedirect.com/science/article/abs/pii/S0925231220309292?via%3Dihub\"},\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"gorriz-ramirez-ortiz-martinezmurcia-segovia-suckling-leming-zhang-etal-artificialintelligencewithintheinterplaybetweennaturalandartificialcomputationadvancesindatasciencetrendsandapplications-2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Measuring and reporting treatment adherence: what can we learn by comparing two respiratory conditions?\",\"type\":\"article\",\"year\":\"2020\",\"volume\":\"(in press)\",\"id\":\"8c638d81-90ef-33f5-885a-6377df82ccc9\",\"created\":\"2020-06-25T21:09:09.432Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-06-25T21:14:15.735Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Tibble, Holly and Flook, Mary and Sheikh, Aziz and Tsanas, Athanasios and Horne, Rob and Geest, Sabina De and Stagg, Helen R\",\"journal\":\"British Journal of Clinical Pharmacology\",\"bibtex\":\"@article{\\n title = {Measuring and reporting treatment adherence: what can we learn by comparing two respiratory conditions?},\\n type = {article},\\n year = {2020},\\n volume = {(in press)},\\n id = {8c638d81-90ef-33f5-885a-6377df82ccc9},\\n created = {2020-06-25T21:09:09.432Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-06-25T21:14:15.735Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Tibble, Holly and Flook, Mary and Sheikh, Aziz and Tsanas, Athanasios and Horne, Rob and Geest, Sabina De and Stagg, Helen R},\\n journal = {British Journal of Clinical Pharmacology}\\n}\",\"author_short\":[\"Tibble,  H.\",\"Flook,  M.\",\"Sheikh,  A.\",\"Tsanas,  A.\",\"Horne,  R.\",\"Geest,  S., D.\",\"Stagg,  H., R.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tibble-flook-sheikh-tsanas-horne-geest-stagg-measuringandreportingtreatmentadherencewhatcanwelearnbycomparingtworespiratoryconditions-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Eye-tracking for longitudinal assessment of social cognition in children born preterm\",\"type\":\"article\",\"year\":\"2020\",\"keywords\":\"digestibility,duodenal flow,milk responses,model evaluation\",\"volume\":\"(in press)\",\"id\":\"82fc8a43-2d17-3700-94fc-2021994c0d1a\",\"created\":\"2020-06-25T21:09:09.452Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-06-25T21:11:25.477Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Dean, Bethan and Ginnell, Lorna and Ledsham, Victoria and Tsanas, Athanasios and Telford, Emma and Sparrow, Sarah and Fletcher-Watson, Sue and Boardman, James\",\"journal\":\"Journal of Child Psychology and Psychiatry\",\"bibtex\":\"@article{\\n title = {Eye-tracking for longitudinal assessment of social cognition in children born preterm},\\n type = {article},\\n year = {2020},\\n keywords = {digestibility,duodenal flow,milk responses,model evaluation},\\n volume = {(in press)},\\n id = {82fc8a43-2d17-3700-94fc-2021994c0d1a},\\n created = {2020-06-25T21:09:09.452Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-06-25T21:11:25.477Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Dean, Bethan and Ginnell, Lorna and Ledsham, Victoria and Tsanas, Athanasios and Telford, Emma and Sparrow, Sarah and Fletcher-Watson, Sue and Boardman, James},\\n journal = {Journal of Child Psychology and Psychiatry}\\n}\",\"author_short\":[\"Dean,  B.\",\"Ginnell,  L.\",\"Ledsham,  V.\",\"Tsanas,  A.\",\"Telford,  E.\",\"Sparrow,  S.\",\"Fletcher-Watson,  S.\",\"Boardman,  J.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"dean-ginnell-ledsham-tsanas-telford-sparrow-fletcherwatson-boardman-eyetrackingforlongitudinalassessmentofsocialcognitioninchildrenbornpreterm-2020\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"digestibility\",\"duodenal flow\",\"milk responses\",\"model evaluation\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Predicting pattern formation in embryonic stem cells using a minimalist, agent‑based probabilistic model\",\"type\":\"article\",\"year\":\"2020\",\"pages\":\"16209\",\"volume\":\"10\",\"websites\":\"https://doi.org/10.1038/s41598-020-73228-4\",\"publisher\":\"Nature Publishing Group UK\",\"id\":\"5c8984b1-aba9-3305-8340-d8a992ff00f5\",\"created\":\"2020-10-02T20:06:54.122Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-11-28T13:57:12.835Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave\",\"doi\":\"10.1038/s41598-020-73228-4\",\"journal\":\"Scientific Reports\",\"bibtex\":\"@article{\\n title = {Predicting pattern formation in embryonic stem cells using a minimalist, agent‑based probabilistic model},\\n type = {article},\\n year = {2020},\\n pages = {16209},\\n volume = {10},\\n websites = {https://doi.org/10.1038/s41598-020-73228-4},\\n publisher = {Nature Publishing Group UK},\\n id = {5c8984b1-aba9-3305-8340-d8a992ff00f5},\\n created = {2020-10-02T20:06:54.122Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-11-28T13:57:12.835Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},\\n doi = {10.1038/s41598-020-73228-4},\\n journal = {Scientific Reports}\\n}\",\"author_short\":[\"Wang,  M.\",\"Tsanas,  A.\",\"Blin,  G.\",\"Robertson,  D.\"],\"urls\":{\"Website\":\"https://doi.org/10.1038/s41598-020-73228-4\"},\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"wang-tsanas-blin-robertson-predictingpatternformationinembryonicstemcellsusingaminimalistagentbasedprobabilisticmodel-2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A data-driven typology of asthma medication adherence using cluster analysis\",\"type\":\"article\",\"year\":\"2020\",\"pages\":\"14999\",\"volume\":\"10\",\"websites\":\"https://doi.org/10.1038/s41598-020-72060-0\",\"publisher\":\"Nature Publishing Group UK\",\"id\":\"55101ab6-04cd-3cba-8893-8dc83be32a1e\",\"created\":\"2020-10-02T20:07:55.135Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-10-02T20:07:55.135Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).\",\"bibtype\":\"article\",\"author\":\"Tibble, Holly and Chan, Amy and Mitchell, Edwin A. and Horne, Elsie and Doudesis, Dimitrios and Horne, Rob and Mizani, Mehrdad A. and Sheikh, Aziz and Tsanas, Athanasios\",\"doi\":\"10.1038/s41598-020-72060-0\",\"journal\":\"Scientific Reports\",\"bibtex\":\"@article{\\n title = {A data-driven typology of asthma medication adherence using cluster analysis},\\n type = {article},\\n year = {2020},\\n pages = {14999},\\n volume = {10},\\n websites = {https://doi.org/10.1038/s41598-020-72060-0},\\n publisher = {Nature Publishing Group UK},\\n id = {55101ab6-04cd-3cba-8893-8dc83be32a1e},\\n created = {2020-10-02T20:07:55.135Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-10-02T20:07:55.135Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).},\\n bibtype = {article},\\n author = {Tibble, Holly and Chan, Amy and Mitchell, Edwin A. and Horne, Elsie and Doudesis, Dimitrios and Horne, Rob and Mizani, Mehrdad A. and Sheikh, Aziz and Tsanas, Athanasios},\\n doi = {10.1038/s41598-020-72060-0},\\n journal = {Scientific Reports}\\n}\",\"author_short\":[\"Tibble,  H.\",\"Chan,  A.\",\"Mitchell,  E., A.\",\"Horne,  E.\",\"Doudesis,  D.\",\"Horne,  R.\",\"Mizani,  M., A.\",\"Sheikh,  A.\",\"Tsanas,  A.\"],\"urls\":{\"Website\":\"https://doi.org/10.1038/s41598-020-72060-0\"},\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tibble-chan-mitchell-horne-doudesis-horne-mizani-sheikh-etal-adatadriventypologyofasthmamedicationadherenceusingclusteranalysis-2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Linkage of Primary Care Prescribing Records and Pharmacy Dispensing Records in Asthma Controller Medications\",\"type\":\"article\",\"year\":\"2020\",\"pages\":\"(in press)\",\"volume\":\"11\",\"id\":\"64cb8abe-6c00-350d-a166-3c90fe376174\",\"created\":\"2020-11-27T18:48:57.930Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-11-27T18:48:57.930Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Background In the UK, issued prescriptions are typically taken to pharmacies, where medications are prepared, recorded, and dispensed. Data Linkage between prescribing and pharmacy dispensing records is not routinely conducted at the individual prescription level for clinical care in England and Wales, however it can be particularly useful for the study of pharmacoepidemiology. With no unique prescribing event identifiers between records, an algorithmic approach is required for this linkage. Aims To create a linkage system for primary care prescribed asthma controller medications and pharmacy dispensing records. Methods Free text labels were used to populate fields for data linkage, relating to medication strength, medication type (active ingredients; allows matching of generic substitutions to named brands), doses per medication unit, prescribed units, and prescribed doses. Prescribing and dispensing records were merged using an inner (many to many) join; generating a candidate link for every combination of records matching on unique patient identifier and medicine. A recursive algorithm was developed and applied, working backwards chronologically through dispensing records and finding the most appropriate match based on the time since prescribing and agreement between the medication description fields. Unmatched records were assessed for quality assurance, and the distribution of linkage strength for matches was examined. Results We developed a harmonisation algorithm in a dataset of over 3 million asthma controller medication prescription records, for which almost 3 in 4 were coded according to the number of units (predominantly inhalers). Incorporating the estimated number of doses prescribed/dispensed into our wider matching algorithm, we were able to find unique prescription records for almost 95% of our dispensing records. Conclusion Early findings demonstrate the accuracy of the developed algorithm linking prescribing and dispensing records. This algorithm can easily be generalised to other conditions.\",\"bibtype\":\"article\",\"author\":\"Tibble, Holly and Lay-Flurrie, James and Sheikh, Aziz and Horne, Robert and Mizani, Mehrdad and Tsanas, Athanasios\",\"doi\":\"10.23889/ijpds.v4i3.1191\",\"journal\":\"BMC Medical Research Methodology\",\"bibtex\":\"@article{\\n title = {Linkage of Primary Care Prescribing Records and Pharmacy Dispensing Records in Asthma Controller Medications},\\n type = {article},\\n year = {2020},\\n pages = {(in press)},\\n volume = {11},\\n id = {64cb8abe-6c00-350d-a166-3c90fe376174},\\n created = {2020-11-27T18:48:57.930Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-11-27T18:48:57.930Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Background In the UK, issued prescriptions are typically taken to pharmacies, where medications are prepared, recorded, and dispensed. Data Linkage between prescribing and pharmacy dispensing records is not routinely conducted at the individual prescription level for clinical care in England and Wales, however it can be particularly useful for the study of pharmacoepidemiology. With no unique prescribing event identifiers between records, an algorithmic approach is required for this linkage. Aims To create a linkage system for primary care prescribed asthma controller medications and pharmacy dispensing records. Methods Free text labels were used to populate fields for data linkage, relating to medication strength, medication type (active ingredients; allows matching of generic substitutions to named brands), doses per medication unit, prescribed units, and prescribed doses. Prescribing and dispensing records were merged using an inner (many to many) join; generating a candidate link for every combination of records matching on unique patient identifier and medicine. A recursive algorithm was developed and applied, working backwards chronologically through dispensing records and finding the most appropriate match based on the time since prescribing and agreement between the medication description fields. Unmatched records were assessed for quality assurance, and the distribution of linkage strength for matches was examined. Results We developed a harmonisation algorithm in a dataset of over 3 million asthma controller medication prescription records, for which almost 3 in 4 were coded according to the number of units (predominantly inhalers). Incorporating the estimated number of doses prescribed/dispensed into our wider matching algorithm, we were able to find unique prescription records for almost 95% of our dispensing records. Conclusion Early findings demonstrate the accuracy of the developed algorithm linking prescribing and dispensing records. This algorithm can easily be generalised to other conditions.},\\n bibtype = {article},\\n author = {Tibble, Holly and Lay-Flurrie, James and Sheikh, Aziz and Horne, Robert and Mizani, Mehrdad and Tsanas, Athanasios},\\n doi = {10.23889/ijpds.v4i3.1191},\\n journal = {BMC Medical Research Methodology}\\n}\",\"author_short\":[\"Tibble,  H.\",\"Lay-Flurrie,  J.\",\"Sheikh,  A.\",\"Horne,  R.\",\"Mizani,  M.\",\"Tsanas,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tibble-layflurrie-sheikh-horne-mizani-tsanas-linkageofprimarycareprescribingrecordsandpharmacydispensingrecordsinasthmacontrollermedications-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Beyond mobile apps: A survey of technologies for mental well-being\",\"type\":\"article\",\"year\":\"2020\",\"keywords\":\"Diagnosis or assessment,Machine learning,Pervasive computing,Physiological Measures,Ubiquitous computing\",\"pages\":\"(in press)\",\"id\":\"19b40068-6b54-33b1-8188-27d8661a1c21\",\"created\":\"2020-11-27T18:53:31.231Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2020-11-27T18:53:31.231Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the development of a robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.\",\"bibtype\":\"article\",\"author\":\"Woodward, K. and Kanjo, E. and Brown, D. and McGinnity, T.M. and Inkster, B. and Macintyre, D.J. and Tsanas, A.\",\"journal\":\"IEEE Transactions on Affective Computing\",\"bibtex\":\"@article{\\n title = {Beyond mobile apps: A survey of technologies for mental well-being},\\n type = {article},\\n year = {2020},\\n keywords = {Diagnosis or assessment,Machine learning,Pervasive computing,Physiological Measures,Ubiquitous computing},\\n pages = {(in press)},\\n id = {19b40068-6b54-33b1-8188-27d8661a1c21},\\n created = {2020-11-27T18:53:31.231Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2020-11-27T18:53:31.231Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the development of a robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.},\\n bibtype = {article},\\n author = {Woodward, K. and Kanjo, E. and Brown, D. and McGinnity, T.M. and Inkster, B. and Macintyre, D.J. and Tsanas, A.},\\n journal = {IEEE Transactions on Affective Computing}\\n}\",\"author_short\":[\"Woodward,  K.\",\"Kanjo,  E.\",\"Brown,  D.\",\"McGinnity,  T.\",\"Inkster,  B.\",\"Macintyre,  D.\",\"Tsanas,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"woodward-kanjo-brown-mcginnity-inkster-macintyre-tsanas-beyondmobileappsasurveyoftechnologiesformentalwellbeing-2020\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Diagnosis or assessment\",\"Machine learning\",\"Pervasive computing\",\"Physiological Measures\",\"Ubiquitous computing\"],\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Performance of monosyllabic vs multisyllabic diadochokinetic exercises in evaluating Parkinson’s disease hypokinetic dysarthria from fuency distributions\",\"type\":\"inproceedings\",\"year\":\"2021\",\"city\":\"Vienna, Austria\",\"id\":\"8f451e19-9e56-3ee0-b8fb-404236e99d1b\",\"created\":\"2021-01-22T08:22:10.239Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2021-01-22T08:26:40.944Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Gómez-Vilda, Pedro and Gomez-Rodellar, Andres and Palacios-Alonso, Daniel and Tsanas, Athanasios\",\"booktitle\":\"14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)\",\"bibtex\":\"@inproceedings{\\n title = {Performance of monosyllabic vs multisyllabic diadochokinetic exercises in evaluating Parkinson’s disease hypokinetic dysarthria from fuency distributions},\\n type = {inproceedings},\\n year = {2021},\\n city = {Vienna, Austria},\\n id = {8f451e19-9e56-3ee0-b8fb-404236e99d1b},\\n created = {2021-01-22T08:22:10.239Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2021-01-22T08:26:40.944Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Gómez-Vilda, Pedro and Gomez-Rodellar, Andres and Palacios-Alonso, Daniel and Tsanas, Athanasios},\\n booktitle = {14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\\n}\",\"author_short\":[\"Gómez-Vilda,  P.\",\"Gomez-Rodellar,  A.\",\"Palacios-Alonso,  D.\",\"Tsanas,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"gmezvilda-gomezrodellar-palaciosalonso-tsanas-performanceofmonosyllabicvsmultisyllabicdiadochokineticexercisesinevaluatingparkinsonsdiseasehypokineticdysarthriafromfuencydistributions-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Remote assessment of Parkinson’s disease symptom severity using the simulated cellular mobile telephone network\",\"type\":\"article\",\"year\":\"2021\",\"pages\":\"11024-11036\",\"volume\":\"9\",\"id\":\"72847093-2e3a-3882-b318-28210ff231cd\",\"created\":\"2021-01-22T08:22:10.243Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2021-01-22T08:25:54.874Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Tsanas, Athanasios and Little, Max A and Ramig, Lorraine O\",\"doi\":\"10.1109/ACCESS.2021.3050524\",\"journal\":\"IEEE Access\",\"bibtex\":\"@article{\\n title = {Remote assessment of Parkinson’s disease symptom severity using the simulated cellular mobile telephone network},\\n type = {article},\\n year = {2021},\\n pages = {11024-11036},\\n volume = {9},\\n id = {72847093-2e3a-3882-b318-28210ff231cd},\\n created = {2021-01-22T08:22:10.243Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2021-01-22T08:25:54.874Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Tsanas, Athanasios and Little, Max A and Ramig, Lorraine O},\\n doi = {10.1109/ACCESS.2021.3050524},\\n journal = {IEEE Access}\\n}\",\"author_short\":[\"Tsanas,  A.\",\"Little,  M., A.\",\"Ramig,  L., O.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tsanas-little-ramig-remoteassessmentofparkinsonsdiseasesymptomseverityusingthesimulatedcellularmobiletelephonenetwork-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Assessing Parkinson’s disease speech signal generalization of clustering results across three countries: findings in the Parkinson’s Voice Initiative study\",\"type\":\"inproceedings\",\"year\":\"2021\",\"city\":\"Vienna, Austria\",\"id\":\"bd3c6bbe-f9fc-3bbf-a302-e85d481ce3d3\",\"created\":\"2021-01-22T08:22:10.304Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2021-01-22T08:26:04.770Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Tsanas, Athanasios and Arora, Siddharth\",\"booktitle\":\"14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)\",\"bibtex\":\"@inproceedings{\\n title = {Assessing Parkinson’s disease speech signal generalization of clustering results across three countries: findings in the Parkinson’s Voice Initiative study},\\n type = {inproceedings},\\n year = {2021},\\n city = {Vienna, Austria},\\n id = {bd3c6bbe-f9fc-3bbf-a302-e85d481ce3d3},\\n created = {2021-01-22T08:22:10.304Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2021-01-22T08:26:04.770Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Tsanas, Athanasios and Arora, Siddharth},\\n booktitle = {14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\\n}\",\"author_short\":[\"Tsanas,  A.\",\"Arora,  S.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"tsanas-arora-assessingparkinsonsdiseasespeechsignalgeneralizationofclusteringresultsacrossthreecountriesfindingsintheparkinsonsvoiceinitiativestudy-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Acoustic to kinematic projection in Parkinson’s disease dysarthria\",\"type\":\"article\",\"year\":\"2021\",\"pages\":\"(in press)\",\"id\":\"b365e48f-0924-34e6-a636-d1da0ee4596e\",\"created\":\"2021-01-27T22:17:49.420Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2021-01-27T22:17:49.420Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Gómez, A and Tsanas, A and Gómez, P and Palacios, D and Rodellar, V and Álvarez, A\",\"journal\":\"Biomedical Signal Processing and Control\",\"bibtex\":\"@article{\\n title = {Acoustic to kinematic projection in Parkinson’s disease dysarthria},\\n type = {article},\\n year = {2021},\\n pages = {(in press)},\\n id = {b365e48f-0924-34e6-a636-d1da0ee4596e},\\n created = {2021-01-27T22:17:49.420Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2021-01-27T22:17:49.420Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Gómez, A and Tsanas, A and Gómez, P and Palacios, D and Rodellar, V and Álvarez, A},\\n journal = {Biomedical Signal Processing and Control}\\n}\",\"author_short\":[\"Gómez,  A.\",\"Tsanas,  A.\",\"Gómez,  P.\",\"Palacios,  D.\",\"Rodellar,  V.\",\"Álvarez,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"gmez-tsanas-gmez-palacios-rodellar-lvarez-acoustictokinematicprojectioninparkinsonsdiseasedysarthria-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Smartphone speech testing for symptom assessment in rapid eye movement sleep behavior disorder and Parkinson’s disease\",\"type\":\"article\",\"year\":\"2021\",\"pages\":\"(in press)\",\"id\":\"94f75525-5517-3225-b1ce-128401b41ff2\",\"created\":\"2021-01-27T22:17:49.424Z\",\"file_attached\":false,\"profile_id\":\"63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"group_id\":\"5c252342-c3f4-3788-aa2e-3a2bba078fe3\",\"last_modified\":\"2021-01-27T22:17:49.424Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Arora, Siddharth and Lo, Christine and Hu, Michele and Tsanas, Athanasios\",\"journal\":\"IEEE Access\",\"bibtex\":\"@article{\\n title = {Smartphone speech testing for symptom assessment in rapid eye movement sleep behavior disorder and Parkinson’s disease},\\n type = {article},\\n year = {2021},\\n pages = {(in press)},\\n id = {94f75525-5517-3225-b1ce-128401b41ff2},\\n created = {2021-01-27T22:17:49.424Z},\\n file_attached = {false},\\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\\n last_modified = {2021-01-27T22:17:49.424Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Arora, Siddharth and Lo, Christine and Hu, Michele and Tsanas, Athanasios},\\n journal = {IEEE Access}\\n}\",\"author_short\":[\"Arora,  S.\",\"Lo,  C.\",\"Hu,  M.\",\"Tsanas,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3\",\"bibbaseid\":\"arora-lo-hu-tsanas-smartphonespeechtestingforsymptomassessmentinrapideyemovementsleepbehaviordisorderandparkinsonsdisease-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"tsanas,  a\":\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\"}},\"downloads\":0,\"html\":\"\"}],\n      metadata: {\"owner\":\"tsanas,  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 title = {Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder},
 type = {article},
 year = {2018},
 pages = {1649},
 volume = {8},
 websites = {http://dx.doi.org/10.1038/s41598-018-19888-9},
 publisher = {Springer US},
 id = {e30f810e-83d0-3682-905a-bc50e7462ab6},
 created = {2019-02-22T10:25:09.690Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2019-02-22T10:29:38.343Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Carr, Oliver and Saunders, Kate E. A. and Tsanas, Athanasios and Palmius, Niclas and Geddes, John R. and Foster, Russell and Goodwin, Guy M. and De Vos, Maarten},
 doi = {10.1038/s41598-018-19888-9},
 journal = {Scientific reports}
}

@article{
 title = {Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment},
 type = {article},
 year = {2017},
 keywords = {bipolar disorder,borderline personality disorder,depression,latent variable structure,mania,mhealth,mobile app,mood monitoring,outcome measures,patient reported},
 pages = {e15},
 volume = {4},
 websites = {http://mental.jmir.org/2017/2/e15/},
 id = {115fd00c-4e7c-3bcb-876d-f5c6a8f5daef},
 created = {2019-02-22T10:25:09.733Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2019-02-22T10:25:09.733Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {BACKGROUND We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. OBJECTIVE The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. METHODS We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. RESULTS We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. CONCLUSIONS This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice.},
 bibtype = {article},
 author = {Tsanas, Athanasios and Saunders, Kate and Bilderbeck, Amy and Palmius, Niclas and Goodwin, Guy and De Vos, Maarten},
 doi = {10.2196/mental.6917},
 journal = {JMIR Mental Health},
 number = {2}
}

@article{
 title = {Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder},
 type = {article},
 year = {2018},
 pages = {79},
 volume = {8},
 id = {e2876a17-b154-3c03-850d-6524c5e45e2f},
 created = {2019-02-22T10:25:09.736Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2019-02-22T10:29:38.355Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2018 The Author(s). It has long been proposed that diurnal rhythms are disturbed in bipolar disorder (BD). Such changes are obvious in episodes of mania or depression. However, detailed study of patients between episodes has been rare and comparison with other psychiatric disorders rarer still. Our hypothesis was that evidence for desynchronization of diurnal rhythms would be evident in BD and that we could test the specificity of any effect by studying borderline personality disorder (BPD). Individuals with BD (n = 36), BPD (n = 22) and healthy volunteers (HC, n = 25) wore a portable heart rate and actigraphy device and used a smart-phone to record self-assessed mood scores 10 times per day for 1 week. Average diurnal patterns of heart rate (HR), activity and sleep were compared within and across groups. Desynchronization in the phase of diurnal rhythms of HR compared with activity were found in BPD (+3 h) and BD (+1 h), but not in HC. A clear diurnal pattern for positive mood was found in all subject groups. The coherence between negative and irritable mood and HR showed a four-cycle per day component in BD and BPD, which was not present in HC. The findings highlight marked de-synchronisation of measured diurnal function in both BD but particularly BPD and suggest an increased association with negative and irritable mood at ultradian frequencies. These findings enhance our understanding of the underlying physiological changes associated with BPD and BD, and suggest objective markers for monitoring and potential treatment targets. Improved mood stabilisation is a translational objective for management of both patient groups.},
 bibtype = {article},
 author = {Carr, O. and Saunders, K.E.A. and Bilderbeck, A.C. and Tsanas, A. and Palmius, N. and Geddes, J.R. and Foster, R. and De Vos, M. and Goodwin, G.M.},
 doi = {10.1038/s41398-018-0125-7},
 journal = {Translational Psychiatry}
}

@article{
 title = {Detecting Bipolar Depression from Geographic Location Data},
 type = {article},
 year = {2017},
 pages = {1761-1771},
 volume = {64},
 websites = {http://ieeexplore.ieee.org/document/7676335/},
 id = {ffc7e80b-6f1d-3422-b1e0-56f67193e1d9},
 created = {2019-02-22T10:25:09.737Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2019-02-22T10:25:09.737Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Palmius, Niclas and Tsanas, Athanasios and Saunders, Kate E. A. and Bilderbeck, Amy C. and Geddes, John R. and Goodwin, Guy M. and De Vos, Maarten},
 doi = {10.1109/TBME.2016.2611862},
 journal = {IEEE Transactions on Biomedical Engineering},
 number = {8}
}

@article{
 title = {Investigating Voice as a Biomarker for leucine-rich repeat kinase 2-Associated Parkinson’s Disease},
 type = {article},
 year = {2018},
 pages = {503-510},
 volume = {8},
 id = {3f57ab1c-f2ea-3e53-beb6-196a618d25fe},
 created = {2019-02-22T10:49:29.986Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2019-02-22T11:11:42.667Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations ('aaah' sounds) were recorded from 7 individuals with LRRK2-associated Parkinson's disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts.},
 bibtype = {article},
 author = {Arora, Siddharth and Visanji, Naomi P and Mestre, Tiago A and Tsanas, Athanasios and Aldakheel, Amaal and Connolly, Barbara S and Gasca-salas, Carmen and Kern, Drew S and Jain, Jennifer and Slow, Elizabeth J and Faust-Socher, Achinoam and Lang, Anthony E and Little, Max A and Marras, Connie},
 doi = {10.3233/JPD-181389},
 journal = {Journal of Parkinson's Disease},
 number = {4}
}

@article{
 title = {High-sensitivity troponin in the evaluation of patients with suspected acute coronary syndrome: a stepped-wedge, cluster-randomised controlled trial},
 type = {article},
 year = {2018},
 pages = {919-928},
 volume = {392},
 id = {09e81b82-c9d8-3b5f-a82b-2e271684e710},
 created = {2019-02-22T10:49:30.089Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2019-02-22T10:49:30.089Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Shah, Anoop S.V. and Anand, Atul and Strachan, Fiona E. and Ferry, Amy V. and Lee, Kuan K. and Chapman, Andrew R. and Sandeman, Dennis and Stables, Catherine L and Adamson, Philip D and Andrews, Jack P M and Anwar, Mohamed S and Hung, John and Moss, Alistair J and O'Brien, Rachel and Berry, Colin and Findlay, Iain and Walker, Simon and Cruickshank, Anne and Reid, Alan and Gray, Alasdair and Collinson, Paul O and Apple, Fred S and McAllister, David A and Maguire, Donogh and Fox, Keith A A and Newby, David E and Tuck, Christopher and Harkess, Ronald and Parker, Richard A and Keerie, Catriona and Weir, Christopher J and Mills, Nicholas L and Investigators, on behalf of the HIGH-STEACS and Mills, Nicholas L and Strachan, Fiona E and Tuck, Christopher and Shah, Anoop S V and Anand, Atul and Ferry, Amy V and Lee, Kuan Ken and Chapman, Andrew R and Sandeman, Dennis and Adamson, Philip D and Stables, Catherine L and Marshall, Lucy and Stewart, Stacey D and Fujisawa, Takeshi and Vallejos, Catalina A and Tsanas, Athanasios and Hautvast, Mischa and McPherson, Jean and McKinlay, Lynn and Newby, David E and Fox, Keith A A and Berry, Colin and Walker, Simon and Weir, Christopher J and Ford, Ian and Gray, Alasdair and Collinson, Paul O and Apple, Fred S and Reid, Alan and Cruikshank, Anne and Findlay, Iain and Amoils, Shannon and McAllister, David A and Maguire, Donogh and Stevens, Jennifer and Norrie, John and Andrews, Jack P M and Adamson, Philip D and Moss, Alastair and Anwar, Mohamed S and Hung, John and Malo, Jonathan and Fischbacher, Colin M and Croal, Bernard L and Leslie, Stephen J and Keerie, Catriona and Parker, Richard A and Walker, Allan and Harkess, Ronnie and Wackett, Tony and Armstrong, Roma and Flood, Marion and Stirling, Laura and MacDonald, Claire and Sadat, Imran and Finlay, Frank and Charles, Heather and Linksted, Pamela and Young, Stephen and Alexander, Bill and Duncan, Chris},
 doi = {10.1016/s0140-6736(18)31923-8},
 journal = {The Lancet},
 number = {10151}
}

@article{
 title = {Euclidean Distances as measures of speaker similarity including identical twin pairs: A forensic investigation using source and filter voice characteristics},
 type = {article},
 year = {2017},
 keywords = {Acoustic analysis,Forensic phonetics,Pause fillers,Perceptual assessment,Twins,Voice quality},
 pages = {25-38},
 volume = {270},
 id = {51c365ff-57d8-3763-8992-b56a9cc2ecd1},
 created = {2019-02-22T11:03:04.627Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2019-02-22T11:03:04.627Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {There is a growing consensus that hybrid approaches are necessary for successful speaker characterization in Forensic Speaker Comparison (FSC); hence this study explores the forensic potential of voice features combining source and filter characteristics. The former relate to the action of the vocal folds while the latter reflect the geometry of the speaker's vocal tract. This set of features have been extracted from pause fillers, which are long enough for robust feature estimation while spontaneous enough to be extracted from voice samples in real forensic casework. Speaker similarity was measured using standardized Euclidean Distances (ED) between pairs of speakers: 54 different-speaker (DS) comparisons, 54 same-speaker (SS) comparisons and 12 comparisons between monozygotic twins (MZ). Results revealed that the differences between DS and SS comparisons were significant in both high quality and telephone-filtered recordings, with no false rejections and limited false acceptances; this finding suggests that this set of voice features is highly speaker-dependent and therefore forensically useful. Mean ED for MZ pairs lies between the average ED for SS comparisons and DS comparisons, as expected according to the literature on twin voices. Specific cases of MZ speakers with very high ED (i.e. strong dissimilarity) are discussed in the context of sociophonetic and twin studies. A preliminary simplification of the Vocal Profile Analysis (VPA) Scheme is proposed, which enables the quantification of voice quality features in the perceptual assessment of speaker similarity, and allows for the calculation of perceptual–acoustic correlations. The adequacy of z-score normalization for this study is also discussed, as well as the relevance of heat maps for detecting the so-called phantoms in recent approaches to the biometric menagerie.},
 bibtype = {article},
 author = {San Segundo, Eugenia and Tsanas, Athanasios and Gómez-Vilda, Pedro},
 doi = {10.1016/j.forsciint.2016.11.020},
 journal = {Forensic Science International}
}

@inproceedings{
 title = {Exploring Pause Fillers in Conversational Speech for Forensic Phonetics: Findings in a Spanish Cohort Including Twins},
 type = {inproceedings},
 year = {2017},
 keywords = {contour,forensic phonetics,fundamental frequency,pause fillers,speech signal processing},
 issue = {July},
 id = {65e7e85a-6877-30ef-991d-018a9bef9339},
 created = {2019-02-22T11:03:04.640Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2019-02-22T11:03:04.640Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Tsanas, A. and San Segundo, E. and Gómez-Vilda, P.},
 doi = {10.1049/cp.2017.0161},
 booktitle = {8th International Conference of Pattern Recognition Systems}
}

@article{
 title = {Quantifying ultrasonic mouse vocalizations using acoustic analysis in a sueprvised statistical machine learning framework},
 type = {article},
 year = {2019},
 pages = {8100},
 volume = {9},
 id = {80ec7b1e-c8a2-38d1-974d-98fc12780f15},
 created = {2019-05-14T23:34:11.757Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2019-06-04T19:05:30.909Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Vogel, Adam and Tsanas, Athanasios and Scattoni, Maria Luisa},
 journal = {Scientific Reports},
 number = {1}
}

@article{
 title = {Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model},
 type = {article},
 year = {2019},
 pages = {e028375},
 volume = {9},
 id = {4d28f322-204f-3f22-abf1-28fee9656249},
 created = {2019-06-04T19:05:30.760Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-02-17T22:34:58.508Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tibble, Holly and Tsanas, Athanasios and Horne, Elsie and Horne, Robert and Mizani, Mehrdad A and Simpson, Colin R and Sheikh, Aziz},
 journal = {BMJ Open},
 number = {7}
}

@article{
 title = {Machine Learning to Predict the Likelihood of Acute Myocardial Infarction},
 type = {article},
 year = {2019},
 keywords = {acute coronary syndrome,infarction,machine learning,myocardial,see page 908,sources of funding,troponin},
 pages = {899-909},
 volume = {140},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/31416346},
 id = {7dfa3078-91b6-31de-8b9d-12fe7af21d1d},
 created = {2019-09-15T10:20:10.856Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-02-17T19:39:57.570Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {BACKGROUND Variations in cardiac troponin concentrations by age, sex and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. METHODS A machine learning algorithm (myocardial-ischemic-injury-index [MI3]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3,013 patients and tested on 7,998 patients with suspected myocardial infarction. MI3 uses gradient boosting to compute a value (0-100) reflecting an individual's likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value (NPV), specificity and positive predictive value (PPV) for that individual. Assessment was by calibration and area under the receiver-operating-characteristic curve (AUC). Secondary analysis evaluated example MI3 thresholds from the training set that identified patients as low-risk (99% sensitivity) and high-risk (75% PPV), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology (ESC) rule-out pathways. RESULTS Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI3 was well calibrated with a very high AUC of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI3 thresholds identifying low-risk and high-risk patients in the training set were 1.6 and 49.7 respectively. In the test set, MI3 values were <1.6 in 69.5% with a NPV of 99.7% (99.5%-99.8%) and sensitivity of 97.8% (96.7-98.7%), and were ≥49.7 in 10.6% with a PPV of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI3 performed better than the ESC 0/3-hour pathway (sensitivity 82.5% [74.5-88.8%], specificity 92.2% [90.7-93.5%]) and the 99th percentile at any time-point (sensitivity 89.6% [87.4-91.6%]), specificity 89.3% [88.6-90.0%]). CONCLUSIONS Using machine learning, MI3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low-risk and high-risk patients who may benefit from earlier clinical decisions. CLINICAL TRIAL REGISTRATION Unique Identifier: Australian New Zealand Clinical Trials Registry: ACTRN12616001441404. URL: https://www.anzctr.org.au.},
 bibtype = {article},
 author = {Than, Martin P and Pickering, John W and Sandoval, Yader and Shah, Anoop S V and Tsanas, Athanasios and Apple, Fred S and Blankenberg, Stefan and Cullen, Louise and Mueller, Christian and Neumann, Johannes T and Twerenbold, Raphael and Westermann, Dirk and Beshiri, Agim and Mills, Nicholas L and MI3 collaborative, undefined},
 doi = {10.1161/CIRCULATIONAHA.119.041980},
 journal = {Circulation}
}

@inproceedings{
 title = {Biomedical speech signal insights from a large scale cohort across seven countries: the Parkinson's voice initiative study},
 type = {inproceedings},
 year = {2019},
 keywords = {parkinson,pd,pvi,s,s disease,speech signal processing,sustained vowel phonations,voice initiative},
 pages = {45-48},
 city = {Florence, Italy},
 id = {8d91e9a7-2a45-3156-b622-40eb83ca9d5d},
 created = {2019-09-15T10:36:53.386Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-02-12T10:49:19.428Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Tsanas, Athanasios and Arora, Siddharth},
 booktitle = {11th International Workshop Models and Analysis of Vocal Emissiong for Biomedical Applications (MAVEBA)}
}

@inproceedings{
 title = {New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity},
 type = {inproceedings},
 year = {2019},
 pages = {3412-3415},
 city = {Berlin, Germany},
 id = {71341c45-b5f6-3384-9cd9-d8ecc00e832d},
 created = {2019-09-15T10:36:53.439Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-02-17T22:34:58.507Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Tsanas, Athanasios},
 booktitle = {41st IEEE Engineering in Medicine and Biology Conference}
}

@inbook{
 type = {inbook},
 year = {2019},
 keywords = {Characterizing stress,Cooperative framework,Data acquisition,Emotional stress,Stress behavior in human-computer interaction},
 pages = {149-160},
 volume = {1148},
 id = {e935baeb-8205-3129-9e87-61515e9542e8},
 created = {2019-09-15T11:14:59.627Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2019-09-15T11:21:21.359Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inbook},
 author = {Palacios-Alonso, Daniel and Lázaro-Carrascosa, Carlos and López-Arribas, Agustín and Meléndez-Morales, Guillermo and Gómez-Rodellar, Andrés and Loro-Álavez, Andrés and Nieto-Lluis, Victor and Rodellar-Biarge, Victoria and Tsanas, Athanasios and Gómez-Vilda, Pedro},
 editor = {Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H.},
 doi = {10.1007/978-3-030-19591-5_16},
 chapter = {Assessing an application of spontaneous stressed speech - emotions portal},
 title = {Understanding the Brain Function and Emotions. IWINAC 2019 (Lecture Notes in Computer Science series)}
}

@inproceedings{
 title = {Exploring telephone-quality speech signals towards parkinson's disease assessment in a large acoustically non-controlled study},
 type = {inproceedings},
 year = {2019},
 keywords = {Data visualization,Dimensionality reduction,Parkinson's Disease (PD),Sustained vowels},
 pages = {953-956},
 id = {efdb297f-c825-3b67-8897-abc9510fc296},
 created = {2020-02-12T10:49:19.200Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-05-08T17:19:02.659Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The use of high-quality speech signals has led to considerable breakthroughs in Parkinson's Disease (PD) in the last decade. These include accurate differentiation of PD versus Healthy Controls (HC) and monitoring longitudinal PD symptom severity. We recently concluded the Parkinson's Voice Initiative (PVI) study collecting data from a very large cohort under non-controlled acoustic conditions. We acoustically characterized 11,942 recordings from 6531 US-based participants using 307 dysphonia measures. We selected a robust subset of 30 dysphonia measures using Gram-Schmidt Orthogonalization (GSO). We projected the data onto a two-dimensional representation using t-distributed stochastic neighbor embedding to facilitate visual exploration, and used hierarchical clustering to understand data homogeneity. We demonstrate that there is considerable overlap in the projected feature space between PD and HC, making the binary classification task particularly challenging. The data was grouped into nine clusters using hierarchical clustering which was in broad agreement with the projected two-dimensional representation. These results provide some new insights into understanding the new challenges posed in the PVI project where acoustic recordings conditions were not controlled.},
 bibtype = {inproceedings},
 author = {Tsanas, Athanasios and Arora, Siddharth},
 doi = {10.1109/BIBE.2019.00178},
 booktitle = {19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)}
}

@article{
 title = {Challenges of clustering multimodal clinical data: a review of applications in asthma subtyping},
 type = {article},
 year = {2020},
 pages = {e16452},
 volume = {8},
 websites = {https://medinform.jmir.org/2020/5/e16452/},
 id = {86649f63-6182-3fb3-92be-e820cb4f3238},
 created = {2020-02-12T10:49:19.267Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-05-28T13:50:36.828Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Horne, Elsie and Tibble, Holly and Sheikh, Aziz and Tsanas, Athanasios},
 doi = {10.2196/16452},
 journal = {JMIR Medical Informatics},
 number = {5}
}

@inproceedings{
 title = {Investigating motility and pattern formation in pluripotent stem cells through agent-based modeling},
 type = {inproceedings},
 year = {2019},
 keywords = {Agent-based modelling,Pattern formation,Pluripotent stem cells},
 pages = {909-913},
 publisher = {IEEE},
 city = {Athens, Greece},
 id = {1451e661-713b-37e7-8658-a48ec10384d1},
 created = {2020-02-17T22:00:12.274Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-05-08T17:19:02.721Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Understanding and predicting the pattern formation in groups of pluripotent stem cells has the potential to improve efficiency and efficacy of stem cell therapies. However, the underlying molecular mechanisms of pluripotent stem cell behaviors are highly complex and are currently still not fully understood. A key practical question is whether deep biological modelling of the cells is essential to predict their pattern formation, or whether there is sufficient predictive power in simply modelling their behaviors and interactions at a higher level. This study focuses on the social interactions and behaviors of pluripotent stem cells at a high-level to predict aggregate crowd behaviors within a level of uncertainty. Agent-based modelling was applied to study the pattern formation in pluripotent stem cells. Five models were established to test four biologically plausible rules of cell motility in terms of: a) velocity, b) directional persistence time, c) directional movements, and d) border effect. We found that it is possible that cells' directional movements based on local density play an important role of the pattern formation, and pattern formation in pluripotent stem cells is governed by a complex combination of rules in our agent-based model simulations, which account for much of the variability observed in experimental findings.},
 bibtype = {inproceedings},
 author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},
 doi = {10.1109/BIBE.2019.00170},
 booktitle = {19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)}
}

@inproceedings{
 title = {Assessing Preferred Proximity Between Different Types of Embryonic Stem Cells},
 type = {inproceedings},
 year = {2020},
 pages = {377-381},
 city = {Valetta, Malta},
 id = {167c26a9-b52c-3181-876a-b72be418ce22},
 created = {2020-02-17T22:00:12.342Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-03-26T14:29:42.035Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},
 booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}
}

@inproceedings{
 title = {Heterogeneity in asthma medication adherence measurement},
 type = {inproceedings},
 year = {2019},
 keywords = {Adherence,Asthma,Electronic monitoring,Medication,Pediatric},
 pages = {899-903},
 city = {Athens, Greece},
 id = {3b61ab79-336d-3a0d-98a4-b972e1204f95},
 created = {2020-02-17T22:00:12.347Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-05-08T17:19:02.655Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Medication non-adherence is strongly associated with poor asthma control and outcomes. Many studies use an aggregate measure of adherence, such as the percentage of prescribed doses that were taken, however this conceals variation between patients' medication-taking routines. Electronic monitoring devices, which precisely record the date and time of a dose being actuated from an inhaler, provide the means to objectively and remotely monitor adherence behavior patterns. This secondary analysis of a New Zealand audio-visual medication reminder intervention study visually explored the relationships, variation, and heterogeneity between multiple measures of adherence, in 211 children aged 6-15 years old who presented to an emergency department with an asthma attack. Our findings highlight the weakness of statistical relationships between measures of adherence, and the irregularity in patient medication-taking behavior. This demonstrates that a single aggregate adherence measure fails to detect asthma patients for whom their day-to-day medication taking (implementation) is inconsistent with their longitudinal medication taking (persistence).},
 bibtype = {inproceedings},
 author = {Tibble, Holly and Chan, Amy and Mitchell, Edwin A. and Horne, Rob and Mizani, Mehrdad A. and Sheikh, Aziz and Tsanas, Athanasios},
 doi = {10.1109/BIBE.2019.00168},
 booktitle = {19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)}
}

@inproceedings{
 title = {Data-driven Insights Towards Risk Assessment of Postpartum Depression},
 type = {inproceedings},
 year = {2020},
 pages = {382-389},
 city = {Valetta, Malta},
 id = {655e8843-eb90-37d6-875b-48addf1d1ed6},
 created = {2020-02-17T22:00:12.362Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-03-26T14:29:42.007Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Valavani, Evdoxia and Doudesis, Dimitrios and Kourtesis, Ioannis and Chin, Richard F.M. and MacIntyre, Donald J. and Fletcher-Watson, Sue and Boardman, James P and Tsanas, Athanasios},
 booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}
}

@inproceedings{
 title = {Large-scale Clustering of People Diagnosed with Parkinson’s Disease using Acoustic Analysis of Sustained Vowels: Findings in the Parkinson’s Voice Initiative Study},
 type = {inproceedings},
 year = {2020},
 pages = {369-376},
 city = {Valetta, Malta},
 id = {f229698d-1895-3236-9406-b9778f8b215a},
 created = {2020-02-17T22:02:31.589Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-06-18T14:02:26.378Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Tsanas, Athanasios and Arora, Siddharth},
 booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}
}

@inproceedings{
 title = {Parkinson’s Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions},
 type = {inproceedings},
 year = {2020},
 pages = {359-368},
 city = {Valetta, Malta},
 id = {fbdbeafc-f820-3905-9e4c-12bc8d6cde5d},
 created = {2020-04-13T10:55:42.288Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-06-27T10:14:10.758Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Álvarez, Agustín and Gómez, Andrés and Palacios, Daniel and Mekyska, Jiri and Tsanas, Athanasios and Gómez, Pedro and Martínez, Rafael},
 doi = {10.5220/0009189403590368},
 booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}
}

@article{
 title = {Objective characterization of activity, sleep, and circadian rhythm patterns using a wrist-worn actigraphy sensor: insights into post-traumatic stress disorder},
 type = {article},
 year = {2020},
 keywords = {actigraphy,geneactiv,posttraumatic stress disorder,sleep,wearable technology},
 pages = {e14306},
 volume = {8},
 websites = {https://mhealth.jmir.org/2020/4/e14306/},
 id = {361f4a71-40ae-3eff-8014-71ba3de15ba1},
 created = {2020-04-30T12:11:42.405Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-04-30T12:11:42.405Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tsanas, Athanasios and Woodward, Elizabeth and Ehlers, Anke},
 doi = {10.2196/14306},
 journal = {JMIR mHealth and uHealth},
 number = {4}
}

@article{
 title = {Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice},
 type = {article},
 year = {2019},
 pages = {2871-2884},
 volume = {145},
 websites = {http://dx.doi.org/10.1121/1.5100272},
 id = {c1bf6321-9df0-395d-a2e2-b489419875bd},
 created = {2020-04-30T12:14:51.819Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-04-30T12:14:51.819Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.},
 bibtype = {article},
 author = {Arora, Siddharth and Baghai-Ravary, Ladan and Tsanas, Athanasios},
 doi = {10.1121/1.5100272},
 journal = {Journal of the Acoustical Society of America},
 number = {5}
}

@article{
 title = {Applications of machine learning in real-life digital health interventions: Review of the literature},
 type = {article},
 year = {2019},
 keywords = {Artificial intelligence,Data mining,Digital health,Machine learning,Review,Telemedicine},
 pages = {1-9},
 volume = {21},
 id = {4addf79b-282a-34d9-9174-7f7a07d3686d},
 created = {2020-04-30T12:14:51.821Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-04-30T12:14:51.821Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Background: Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals. Objective: Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain. Methods: We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction). Results: Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes. Conclusions: This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.},
 bibtype = {article},
 author = {Triantafyllidis, Andreas K. and Tsanas, Athanasios},
 doi = {10.2196/12286},
 journal = {Journal of Medical Internet Research},
 number = {4}
}

@article{
 title = {Telemedicine Cognitive Behavioural Therapy for Anxiety after Stroke: Proof-of-Concept Randomized Controlled Trial},
 type = {article},
 year = {2020},
 pages = {2297-2306},
 volume = {51},
 id = {5e8fd247-1354-38c5-bc39-02238bec766a},
 created = {2020-05-08T17:12:49.455Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-08-22T12:49:11.720Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Chun, H-Y. Yvonne and Carson, Alan J and Tsanas, Athanasios and Dennis, Martin S and Mead, Gillian E and Whiteley, William N},
 journal = {Stroke}
}

@article{
 title = {Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications},
 type = {article},
 year = {2020},
 pages = {237-270},
 volume = {410},
 websites = {https://www.sciencedirect.com/science/article/abs/pii/S0925231220309292?via%3Dihub},
 id = {48a29a5b-f09b-3ffc-ab3f-17558dc46ef8},
 created = {2020-05-27T09:42:19.096Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-08-25T16:21:03.340Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Gorriz, Juan M and Ramirez, Javier and Ortiz, Andres and Martinez-Murcia, Francisco J and Segovia, Fermin and Suckling, John and Leming, Matthew and Zhang, Yu-Dong and Alvarez-Sanchez, Jose Ramon and Bologna, Guido and Bonomini, Paula and Casado, Fernando E and Charte, David and Charte, Francisco and Contreras, Ricardo and Cuesta-Infante, Alfredo and Duro, Richard J and Fernandez-Caballero, Antonio and Fernandez-Jover, Eduardo and Gomez-Vilda, Pedro and Grana, Manuel and Herrera, Francisco and Iglesias, Roberto and Lekova, Anna and de Lope, Javier and Lopez-Rubio, Ezequiel and Martinez Tomas, Rafael and Molina-Cabello, Miguel A and Montemayor, Antonio S and Novais, Paulo and Palacios-Alonso, Daniel and Pantrigo, Juaj J and Payne, Bryson R and de la Paz Lopez, Felix and Angelica Pinninghoff, Maria and Rincon, Mariano and Sanstos, Jose and Thurnhofer-Hemsi, Karl and Tsanas, Athanasios and Varela, Ramiro and Ferrandez, Jose M},
 journal = {Neurocomputing}
}

@article{
 title = {Measuring and reporting treatment adherence: what can we learn by comparing two respiratory conditions?},
 type = {article},
 year = {2020},
 volume = {(in press)},
 id = {8c638d81-90ef-33f5-885a-6377df82ccc9},
 created = {2020-06-25T21:09:09.432Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-06-25T21:14:15.735Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tibble, Holly and Flook, Mary and Sheikh, Aziz and Tsanas, Athanasios and Horne, Rob and Geest, Sabina De and Stagg, Helen R},
 journal = {British Journal of Clinical Pharmacology}
}

@article{
 title = {Eye-tracking for longitudinal assessment of social cognition in children born preterm},
 type = {article},
 year = {2020},
 keywords = {digestibility,duodenal flow,milk responses,model evaluation},
 volume = {(in press)},
 id = {82fc8a43-2d17-3700-94fc-2021994c0d1a},
 created = {2020-06-25T21:09:09.452Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-06-25T21:11:25.477Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Dean, Bethan and Ginnell, Lorna and Ledsham, Victoria and Tsanas, Athanasios and Telford, Emma and Sparrow, Sarah and Fletcher-Watson, Sue and Boardman, James},
 journal = {Journal of Child Psychology and Psychiatry}
}

@article{
 title = {Predicting pattern formation in embryonic stem cells using a minimalist, agent‑based probabilistic model},
 type = {article},
 year = {2020},
 pages = {16209},
 volume = {10},
 websites = {https://doi.org/10.1038/s41598-020-73228-4},
 publisher = {Nature Publishing Group UK},
 id = {5c8984b1-aba9-3305-8340-d8a992ff00f5},
 created = {2020-10-02T20:06:54.122Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-11-28T13:57:12.835Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},
 doi = {10.1038/s41598-020-73228-4},
 journal = {Scientific Reports}
}

@article{
 title = {A data-driven typology of asthma medication adherence using cluster analysis},
 type = {article},
 year = {2020},
 pages = {14999},
 volume = {10},
 websites = {https://doi.org/10.1038/s41598-020-72060-0},
 publisher = {Nature Publishing Group UK},
 id = {55101ab6-04cd-3cba-8893-8dc83be32a1e},
 created = {2020-10-02T20:07:55.135Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-10-02T20:07:55.135Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).},
 bibtype = {article},
 author = {Tibble, Holly and Chan, Amy and Mitchell, Edwin A. and Horne, Elsie and Doudesis, Dimitrios and Horne, Rob and Mizani, Mehrdad A. and Sheikh, Aziz and Tsanas, Athanasios},
 doi = {10.1038/s41598-020-72060-0},
 journal = {Scientific Reports}
}

@article{
 title = {Linkage of Primary Care Prescribing Records and Pharmacy Dispensing Records in Asthma Controller Medications},
 type = {article},
 year = {2020},
 pages = {(in press)},
 volume = {11},
 id = {64cb8abe-6c00-350d-a166-3c90fe376174},
 created = {2020-11-27T18:48:57.930Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-11-27T18:48:57.930Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Background In the UK, issued prescriptions are typically taken to pharmacies, where medications are prepared, recorded, and dispensed. Data Linkage between prescribing and pharmacy dispensing records is not routinely conducted at the individual prescription level for clinical care in England and Wales, however it can be particularly useful for the study of pharmacoepidemiology. With no unique prescribing event identifiers between records, an algorithmic approach is required for this linkage. Aims To create a linkage system for primary care prescribed asthma controller medications and pharmacy dispensing records. Methods Free text labels were used to populate fields for data linkage, relating to medication strength, medication type (active ingredients; allows matching of generic substitutions to named brands), doses per medication unit, prescribed units, and prescribed doses. Prescribing and dispensing records were merged using an inner (many to many) join; generating a candidate link for every combination of records matching on unique patient identifier and medicine. A recursive algorithm was developed and applied, working backwards chronologically through dispensing records and finding the most appropriate match based on the time since prescribing and agreement between the medication description fields. Unmatched records were assessed for quality assurance, and the distribution of linkage strength for matches was examined. Results We developed a harmonisation algorithm in a dataset of over 3 million asthma controller medication prescription records, for which almost 3 in 4 were coded according to the number of units (predominantly inhalers). Incorporating the estimated number of doses prescribed/dispensed into our wider matching algorithm, we were able to find unique prescription records for almost 95% of our dispensing records. Conclusion Early findings demonstrate the accuracy of the developed algorithm linking prescribing and dispensing records. This algorithm can easily be generalised to other conditions.},
 bibtype = {article},
 author = {Tibble, Holly and Lay-Flurrie, James and Sheikh, Aziz and Horne, Robert and Mizani, Mehrdad and Tsanas, Athanasios},
 doi = {10.23889/ijpds.v4i3.1191},
 journal = {BMC Medical Research Methodology}
}

@article{
 title = {Beyond mobile apps: A survey of technologies for mental well-being},
 type = {article},
 year = {2020},
 keywords = {Diagnosis or assessment,Machine learning,Pervasive computing,Physiological Measures,Ubiquitous computing},
 pages = {(in press)},
 id = {19b40068-6b54-33b1-8188-27d8661a1c21},
 created = {2020-11-27T18:53:31.231Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2020-11-27T18:53:31.231Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the development of a robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.},
 bibtype = {article},
 author = {Woodward, K. and Kanjo, E. and Brown, D. and McGinnity, T.M. and Inkster, B. and Macintyre, D.J. and Tsanas, A.},
 journal = {IEEE Transactions on Affective Computing}
}

@inproceedings{
 title = {Performance of monosyllabic vs multisyllabic diadochokinetic exercises in evaluating Parkinson’s disease hypokinetic dysarthria from fuency distributions},
 type = {inproceedings},
 year = {2021},
 city = {Vienna, Austria},
 id = {8f451e19-9e56-3ee0-b8fb-404236e99d1b},
 created = {2021-01-22T08:22:10.239Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2021-01-22T08:26:40.944Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Gómez-Vilda, Pedro and Gomez-Rodellar, Andres and Palacios-Alonso, Daniel and Tsanas, Athanasios},
 booktitle = {14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}
}

@article{
 title = {Remote assessment of Parkinson’s disease symptom severity using the simulated cellular mobile telephone network},
 type = {article},
 year = {2021},
 pages = {11024-11036},
 volume = {9},
 id = {72847093-2e3a-3882-b318-28210ff231cd},
 created = {2021-01-22T08:22:10.243Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2021-01-22T08:25:54.874Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tsanas, Athanasios and Little, Max A and Ramig, Lorraine O},
 doi = {10.1109/ACCESS.2021.3050524},
 journal = {IEEE Access}
}

@inproceedings{
 title = {Assessing Parkinson’s disease speech signal generalization of clustering results across three countries: findings in the Parkinson’s Voice Initiative study},
 type = {inproceedings},
 year = {2021},
 city = {Vienna, Austria},
 id = {bd3c6bbe-f9fc-3bbf-a302-e85d481ce3d3},
 created = {2021-01-22T08:22:10.304Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2021-01-22T08:26:04.770Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Tsanas, Athanasios and Arora, Siddharth},
 booktitle = {14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}
}

@article{
 title = {Acoustic to kinematic projection in Parkinson’s disease dysarthria},
 type = {article},
 year = {2021},
 pages = {(in press)},
 id = {b365e48f-0924-34e6-a636-d1da0ee4596e},
 created = {2021-01-27T22:17:49.420Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2021-01-27T22:17:49.420Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Gómez, A and Tsanas, A and Gómez, P and Palacios, D and Rodellar, V and Álvarez, A},
 journal = {Biomedical Signal Processing and Control}
}

@article{
 title = {Smartphone speech testing for symptom assessment in rapid eye movement sleep behavior disorder and Parkinson’s disease},
 type = {article},
 year = {2021},
 pages = {(in press)},
 id = {94f75525-5517-3225-b1ce-128401b41ff2},
 created = {2021-01-27T22:17:49.424Z},
 file_attached = {false},
 profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},
 group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},
 last_modified = {2021-01-27T22:17:49.424Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Arora, Siddharth and Lo, Christine and Hu, Michele and Tsanas, Athanasios},
 journal = {IEEE Access}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! Next you can\n      <a href=\"/network/register?next=/network/newSiteFromTemplate%3Fbiburl=\"\n      >create a new website with this list</a>, or\n      embed it in an existing web page by copying &amp; pasting\n      any of the following snippets.\n\n      <div style=\"text-align: left; margin-top: 1em;\">\n        <strong>JavaScript</strong>\n        <span class=\"comment recommended\">(easiest)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;script src=\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3/group/5c252342-c3f4-3788-aa2e-3a2bba078fe3?jsonp=1&amp;jsonp=1\"&gt;&lt;/script&gt;\n          </code>\n        </div>\n\n        <strong>PHP</strong>\n        <div class=\"well well-small\">\n          <code>\n            &lt;?php\n            $contents = file_get_contents(\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3/group/5c252342-c3f4-3788-aa2e-3a2bba078fe3?jsonp=1\");\n            print_r($contents);\n            ?&gt;\n          </code>\n        </div>\n\n        <strong>iFrame</strong>\n        <span class=\"comment\">(not recommended)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;iframe src=\"https://bibbase.org/service/mendeley/63c14ecf-8d9b-3014-983a-e77627cb99e3/group/5c252342-c3f4-3788-aa2e-3a2bba078fe3?jsonp=1\"&gt;&lt;/iframe&gt;\n          </code>\n        </div>\n\n        <p>\n          For more details see the <a href=\"//bibbase.org/help\">documention</a>.\n        </p>\n      </div>\n    </div>\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_preview\"\n         style=\"display: none;\">\n\n      This is a preview! To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (5)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"gmezvilda-gomezrodellar-palaciosalonso-tsanas-performanceofmonosyllabicvsmultisyllabicdiadochokineticexercisesinevaluatingparkinsonsdiseasehypokineticdysarthriafromfuencydistributions-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Performance of monosyllabic vs multisyllabic diadochokinetic exercises in evaluating Parkinson’s disease hypokinetic dysarthria from fuency distributions.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gómez-Vilda,  P.; Gomez-Rodellar,  A.; Palacios-Alonso,  D.;  and <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)</i>,  2021. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gmezvilda-gomezrodellar-palaciosalonso-tsanas-performanceofmonosyllabicvsmultisyllabicdiadochokineticexercisesinevaluatingparkinsonsdiseasehypokineticdysarthriafromfuencydistributions-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gmezvilda-gomezrodellar-palaciosalonso-tsanas-performanceofmonosyllabicvsmultisyllabicdiadochokineticexercisesinevaluatingparkinsonsdiseasehypokineticdysarthriafromfuencydistributions-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Performance of monosyllabic vs multisyllabic diadochokinetic exercises in evaluating Parkinson’s disease hypokinetic dysarthria from fuency distributions},\n type = {inproceedings},\n year = {2021},\n city = {Vienna, Austria},\n id = {8f451e19-9e56-3ee0-b8fb-404236e99d1b},\n created = {2021-01-22T08:22:10.239Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2021-01-22T08:26:40.944Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Gómez-Vilda, Pedro and Gomez-Rodellar, Andres and Palacios-Alonso, Daniel and Tsanas, Athanasios},\n booktitle = {14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tsanas-little-ramig-remoteassessmentofparkinsonsdiseasesymptomseverityusingthesimulatedcellularmobiletelephonenetwork-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Remote assessment of Parkinson’s disease symptom severity using the simulated cellular mobile telephone network.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Little,  M., A.;  and Ramig,  L., O.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Access</i>, 9: 11024-11036.  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ACCESS.2021.3050524\"\n     onclick=\"log_download('tsanas-little-ramig-remoteassessmentofparkinsonsdiseasesymptomseverityusingthesimulatedcellularmobiletelephonenetwork-2021', 'http://doi.org/10.1109/ACCESS.2021.3050524', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsanas-little-ramig-remoteassessmentofparkinsonsdiseasesymptomseverityusingthesimulatedcellularmobiletelephonenetwork-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsanas-little-ramig-remoteassessmentofparkinsonsdiseasesymptomseverityusingthesimulatedcellularmobiletelephonenetwork-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Remote assessment of Parkinson’s disease symptom severity using the simulated cellular mobile telephone network},\n type = {article},\n year = {2021},\n pages = {11024-11036},\n volume = {9},\n id = {72847093-2e3a-3882-b318-28210ff231cd},\n created = {2021-01-22T08:22:10.243Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2021-01-22T08:25:54.874Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Tsanas, Athanasios and Little, Max A and Ramig, Lorraine O},\n doi = {10.1109/ACCESS.2021.3050524},\n journal = {IEEE Access}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tsanas-arora-assessingparkinsonsdiseasespeechsignalgeneralizationofclusteringresultsacrossthreecountriesfindingsintheparkinsonsvoiceinitiativestudy-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Assessing Parkinson’s disease speech signal generalization of clustering results across three countries: findings in the Parkinson’s Voice Initiative study.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>;  and Arora,  S.\n</span>\n\n  \n\n</span>\n\n  In <i>14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)</i>,  2021. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsanas-arora-assessingparkinsonsdiseasespeechsignalgeneralizationofclusteringresultsacrossthreecountriesfindingsintheparkinsonsvoiceinitiativestudy-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsanas-arora-assessingparkinsonsdiseasespeechsignalgeneralizationofclusteringresultsacrossthreecountriesfindingsintheparkinsonsvoiceinitiativestudy-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Assessing Parkinson’s disease speech signal generalization of clustering results across three countries: findings in the Parkinson’s Voice Initiative study},\n type = {inproceedings},\n year = {2021},\n city = {Vienna, Austria},\n id = {bd3c6bbe-f9fc-3bbf-a302-e85d481ce3d3},\n created = {2021-01-22T08:22:10.304Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2021-01-22T08:26:04.770Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Tsanas, Athanasios and Arora, Siddharth},\n booktitle = {14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"gmez-tsanas-gmez-palacios-rodellar-lvarez-acoustictokinematicprojectioninparkinsonsdiseasedysarthria-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Acoustic to kinematic projection in Parkinson’s disease dysarthria.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gómez,  A.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Gómez,  P.; Palacios,  D.; Rodellar,  V.;  and Álvarez,  A.\n</span>\n\n  \n\n</span>\n\n  <i>Biomedical Signal Processing and Control</i>,(in press).  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gmez-tsanas-gmez-palacios-rodellar-lvarez-acoustictokinematicprojectioninparkinsonsdiseasedysarthria-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gmez-tsanas-gmez-palacios-rodellar-lvarez-acoustictokinematicprojectioninparkinsonsdiseasedysarthria-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Acoustic to kinematic projection in Parkinson’s disease dysarthria},\n type = {article},\n year = {2021},\n pages = {(in press)},\n id = {b365e48f-0924-34e6-a636-d1da0ee4596e},\n created = {2021-01-27T22:17:49.420Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2021-01-27T22:17:49.420Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Gómez, A and Tsanas, A and Gómez, P and Palacios, D and Rodellar, V and Álvarez, A},\n journal = {Biomedical Signal Processing and Control}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"arora-lo-hu-tsanas-smartphonespeechtestingforsymptomassessmentinrapideyemovementsleepbehaviordisorderandparkinsonsdisease-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Smartphone speech testing for symptom assessment in rapid eye movement sleep behavior disorder and Parkinson’s disease.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Arora,  S.; Lo,  C.; Hu,  M.;  and <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Access</i>,(in press).  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/arora-lo-hu-tsanas-smartphonespeechtestingforsymptomassessmentinrapideyemovementsleepbehaviordisorderandparkinsonsdisease-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('arora-lo-hu-tsanas-smartphonespeechtestingforsymptomassessmentinrapideyemovementsleepbehaviordisorderandparkinsonsdisease-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Smartphone speech testing for symptom assessment in rapid eye movement sleep behavior disorder and Parkinson’s disease},\n type = {article},\n year = {2021},\n pages = {(in press)},\n id = {94f75525-5517-3225-b1ce-128401b41ff2},\n created = {2021-01-27T22:17:49.424Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2021-01-27T22:17:49.424Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Arora, Siddharth and Lo, Christine and Hu, Michele and Tsanas, Athanasios},\n journal = {IEEE Access}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (14)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"horne-tibble-sheikh-tsanas-challengesofclusteringmultimodalclinicaldataareviewofapplicationsinasthmasubtyping-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://medinform.jmir.org/2020/5/e16452/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'horne-tibble-sheikh-tsanas-challengesofclusteringmultimodalclinicaldataareviewofapplicationsinasthmasubtyping-2020', 'https://medinform.jmir.org/2020/5/e16452/', event);\">\n    Challenges of clustering multimodal clinical data: a review of applications in asthma subtyping.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Horne,  E.; Tibble,  H.; Sheikh,  A.;  and <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  <i>JMIR Medical Informatics</i>, 8(5): e16452.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://medinform.jmir.org/2020/5/e16452/\"\n     onclick=\"log_download('horne-tibble-sheikh-tsanas-challengesofclusteringmultimodalclinicaldataareviewofapplicationsinasthmasubtyping-2020', 'https://medinform.jmir.org/2020/5/e16452/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Challenges of clustering multimodal clinical data: a review of applications in asthma subtyping [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2196/16452\"\n     onclick=\"log_download('horne-tibble-sheikh-tsanas-challengesofclusteringmultimodalclinicaldataareviewofapplicationsinasthmasubtyping-2020', 'http://doi.org/10.2196/16452', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/horne-tibble-sheikh-tsanas-challengesofclusteringmultimodalclinicaldataareviewofapplicationsinasthmasubtyping-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('horne-tibble-sheikh-tsanas-challengesofclusteringmultimodalclinicaldataareviewofapplicationsinasthmasubtyping-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Challenges of clustering multimodal clinical data: a review of applications in asthma subtyping},\n type = {article},\n year = {2020},\n pages = {e16452},\n volume = {8},\n websites = {https://medinform.jmir.org/2020/5/e16452/},\n id = {86649f63-6182-3fb3-92be-e820cb4f3238},\n created = {2020-02-12T10:49:19.267Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-05-28T13:50:36.828Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Horne, Elsie and Tibble, Holly and Sheikh, Aziz and Tsanas, Athanasios},\n doi = {10.2196/16452},\n journal = {JMIR Medical Informatics},\n number = {5}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wang-tsanas-blin-robertson-assessingpreferredproximitybetweendifferenttypesofembryonicstemcells-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Assessing Preferred Proximity Between Different Types of Embryonic Stem Cells.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang,  M.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Blin,  G.;  and Robertson,  D.\n</span>\n\n  \n\n</span>\n\n  In <i>13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)</i>, pages 377-381,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-tsanas-blin-robertson-assessingpreferredproximitybetweendifferenttypesofembryonicstemcells-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-tsanas-blin-robertson-assessingpreferredproximitybetweendifferenttypesofembryonicstemcells-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Assessing Preferred Proximity Between Different Types of Embryonic Stem Cells},\n type = {inproceedings},\n year = {2020},\n pages = {377-381},\n city = {Valetta, Malta},\n id = {167c26a9-b52c-3181-876a-b72be418ce22},\n created = {2020-02-17T22:00:12.342Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-03-26T14:29:42.035Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},\n booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"valavani-doudesis-kourtesis-chin-macintyre-fletcherwatson-boardman-tsanas-datadriveninsightstowardsriskassessmentofpostpartumdepression-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Data-driven Insights Towards Risk Assessment of Postpartum Depression.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Valavani,  E.; Doudesis,  D.; Kourtesis,  I.; Chin,  R., F.; MacIntyre,  D., J.; Fletcher-Watson,  S.; Boardman,  J., P.;  and <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)</i>, pages 382-389,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/valavani-doudesis-kourtesis-chin-macintyre-fletcherwatson-boardman-tsanas-datadriveninsightstowardsriskassessmentofpostpartumdepression-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('valavani-doudesis-kourtesis-chin-macintyre-fletcherwatson-boardman-tsanas-datadriveninsightstowardsriskassessmentofpostpartumdepression-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Data-driven Insights Towards Risk Assessment of Postpartum Depression},\n type = {inproceedings},\n year = {2020},\n pages = {382-389},\n city = {Valetta, Malta},\n id = {655e8843-eb90-37d6-875b-48addf1d1ed6},\n created = {2020-02-17T22:00:12.362Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-03-26T14:29:42.007Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Valavani, Evdoxia and Doudesis, Dimitrios and Kourtesis, Ioannis and Chin, Richard F.M. and MacIntyre, Donald J. and Fletcher-Watson, Sue and Boardman, James P and Tsanas, Athanasios},\n booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tsanas-arora-largescaleclusteringofpeoplediagnosedwithparkinsonsdiseaseusingacousticanalysisofsustainedvowelsfindingsintheparkinsonsvoiceinitiativestudy-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Large-scale Clustering of People Diagnosed with Parkinson’s Disease using Acoustic Analysis of Sustained Vowels: Findings in the Parkinson’s Voice Initiative Study.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>;  and Arora,  S.\n</span>\n\n  \n\n</span>\n\n  In <i>13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)</i>, pages 369-376,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsanas-arora-largescaleclusteringofpeoplediagnosedwithparkinsonsdiseaseusingacousticanalysisofsustainedvowelsfindingsintheparkinsonsvoiceinitiativestudy-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsanas-arora-largescaleclusteringofpeoplediagnosedwithparkinsonsdiseaseusingacousticanalysisofsustainedvowelsfindingsintheparkinsonsvoiceinitiativestudy-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Large-scale Clustering of People Diagnosed with Parkinson’s Disease using Acoustic Analysis of Sustained Vowels: Findings in the Parkinson’s Voice Initiative Study},\n type = {inproceedings},\n year = {2020},\n pages = {369-376},\n city = {Valetta, Malta},\n id = {f229698d-1895-3236-9406-b9778f8b215a},\n created = {2020-02-17T22:02:31.589Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-06-18T14:02:26.378Z},\n read = {true},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Tsanas, Athanasios and Arora, Siddharth},\n booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lvarez-gmez-palacios-mekyska-tsanas-gmez-martnez-parkinsonsdiseaseglottalflowcharacterizationphonationfeaturesvsamplitudedistributions-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Parkinson’s Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Álvarez,  A.; Gómez,  A.; Palacios,  D.; Mekyska,  J.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Gómez,  P.;  and Martínez,  R.\n</span>\n\n  \n\n</span>\n\n  In <i>13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)</i>, pages 359-368,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.5220/0009189403590368\"\n     onclick=\"log_download('lvarez-gmez-palacios-mekyska-tsanas-gmez-martnez-parkinsonsdiseaseglottalflowcharacterizationphonationfeaturesvsamplitudedistributions-2020', 'http://doi.org/10.5220/0009189403590368', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lvarez-gmez-palacios-mekyska-tsanas-gmez-martnez-parkinsonsdiseaseglottalflowcharacterizationphonationfeaturesvsamplitudedistributions-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lvarez-gmez-palacios-mekyska-tsanas-gmez-martnez-parkinsonsdiseaseglottalflowcharacterizationphonationfeaturesvsamplitudedistributions-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Parkinson’s Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions},\n type = {inproceedings},\n year = {2020},\n pages = {359-368},\n city = {Valetta, Malta},\n id = {fbdbeafc-f820-3905-9e4c-12bc8d6cde5d},\n created = {2020-04-13T10:55:42.288Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-06-27T10:14:10.758Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Álvarez, Agustín and Gómez, Andrés and Palacios, Daniel and Mekyska, Jiri and Tsanas, Athanasios and Gómez, Pedro and Martínez, Rafael},\n doi = {10.5220/0009189403590368},\n booktitle = {13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tsanas-woodward-ehlers-objectivecharacterizationofactivitysleepandcircadianrhythmpatternsusingawristwornactigraphysensorinsightsintoposttraumaticstressdisorder-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://mhealth.jmir.org/2020/4/e14306/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'tsanas-woodward-ehlers-objectivecharacterizationofactivitysleepandcircadianrhythmpatternsusingawristwornactigraphysensorinsightsintoposttraumaticstressdisorder-2020', 'https://mhealth.jmir.org/2020/4/e14306/', event);\">\n    Objective characterization of activity, sleep, and circadian rhythm patterns using a wrist-worn actigraphy sensor: insights into post-traumatic stress disorder.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Woodward,  E.;  and Ehlers,  A.\n</span>\n\n  \n\n</span>\n\n  <i>JMIR mHealth and uHealth</i>, 8(4): e14306.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://mhealth.jmir.org/2020/4/e14306/\"\n     onclick=\"log_download('tsanas-woodward-ehlers-objectivecharacterizationofactivitysleepandcircadianrhythmpatternsusingawristwornactigraphysensorinsightsintoposttraumaticstressdisorder-2020', 'https://mhealth.jmir.org/2020/4/e14306/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Objective characterization of activity, sleep, and circadian rhythm patterns using a wrist-worn actigraphy sensor: insights into post-traumatic stress disorder [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2196/14306\"\n     onclick=\"log_download('tsanas-woodward-ehlers-objectivecharacterizationofactivitysleepandcircadianrhythmpatternsusingawristwornactigraphysensorinsightsintoposttraumaticstressdisorder-2020', 'http://doi.org/10.2196/14306', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsanas-woodward-ehlers-objectivecharacterizationofactivitysleepandcircadianrhythmpatternsusingawristwornactigraphysensorinsightsintoposttraumaticstressdisorder-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsanas-woodward-ehlers-objectivecharacterizationofactivitysleepandcircadianrhythmpatternsusingawristwornactigraphysensorinsightsintoposttraumaticstressdisorder-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Objective characterization of activity, sleep, and circadian rhythm patterns using a wrist-worn actigraphy sensor: insights into post-traumatic stress disorder},\n type = {article},\n year = {2020},\n keywords = {actigraphy,geneactiv,posttraumatic stress disorder,sleep,wearable technology},\n pages = {e14306},\n volume = {8},\n websites = {https://mhealth.jmir.org/2020/4/e14306/},\n id = {361f4a71-40ae-3eff-8014-71ba3de15ba1},\n created = {2020-04-30T12:11:42.405Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-04-30T12:11:42.405Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Tsanas, Athanasios and Woodward, Elizabeth and Ehlers, Anke},\n doi = {10.2196/14306},\n journal = {JMIR mHealth and uHealth},\n number = {4}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chun-carson-tsanas-dennis-mead-whiteley-telemedicinecognitivebehaviouraltherapyforanxietyafterstrokeproofofconceptrandomizedcontrolledtrial-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Telemedicine Cognitive Behavioural Therapy for Anxiety after Stroke: Proof-of-Concept Randomized Controlled Trial.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chun,  H., Y.; Carson,  A., J.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Dennis,  M., S.; Mead,  G., E.;  and Whiteley,  W., N.\n</span>\n\n  \n\n</span>\n\n  <i>Stroke</i>, 51: 2297-2306.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chun-carson-tsanas-dennis-mead-whiteley-telemedicinecognitivebehaviouraltherapyforanxietyafterstrokeproofofconceptrandomizedcontrolledtrial-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chun-carson-tsanas-dennis-mead-whiteley-telemedicinecognitivebehaviouraltherapyforanxietyafterstrokeproofofconceptrandomizedcontrolledtrial-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Telemedicine Cognitive Behavioural Therapy for Anxiety after Stroke: Proof-of-Concept Randomized Controlled Trial},\n type = {article},\n year = {2020},\n pages = {2297-2306},\n volume = {51},\n id = {5e8fd247-1354-38c5-bc39-02238bec766a},\n created = {2020-05-08T17:12:49.455Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-08-22T12:49:11.720Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Chun, H-Y. Yvonne and Carson, Alan J and Tsanas, Athanasios and Dennis, Martin S and Mead, Gillian E and Whiteley, William N},\n journal = {Stroke}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"gorriz-ramirez-ortiz-martinezmurcia-segovia-suckling-leming-zhang-etal-artificialintelligencewithintheinterplaybetweennaturalandartificialcomputationadvancesindatasciencetrendsandapplications-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/abs/pii/S0925231220309292?via%3Dihub\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'gorriz-ramirez-ortiz-martinezmurcia-segovia-suckling-leming-zhang-etal-artificialintelligencewithintheinterplaybetweennaturalandartificialcomputationadvancesindatasciencetrendsandapplications-2020', 'https://www.sciencedirect.com/science/article/abs/pii/S0925231220309292?via%3Dihub', event);\">\n    Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gorriz,  J., M.; Ramirez,  J.; Ortiz,  A.; Martinez-Murcia,  F., J.; Segovia,  F.; Suckling,  J.; Leming,  M.; Zhang,  Y.; Alvarez-Sanchez,  J., R.; Bologna,  G.; Bonomini,  P.; Casado,  F., E.; Charte,  D.; Charte,  F.; Contreras,  R.; Cuesta-Infante,  A.; Duro,  R., J.; Fernandez-Caballero,  A.; Fernandez-Jover,  E.; Gomez-Vilda,  P.; Grana,  M.; Herrera,  F.; Iglesias,  R.; Lekova,  A.; de Lope,  J.; Lopez-Rubio,  E.; Martinez Tomas,  R.; Molina-Cabello,  M., A.; Montemayor,  A., S.; Novais,  P.; Palacios-Alonso,  D.; Pantrigo,  J., J.; Payne,  B., R.; de la Paz Lopez,  F.; Angelica Pinninghoff,  M.; Rincon,  M.; Sanstos,  J.; Thurnhofer-Hemsi,  K.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Varela,  R.;  and Ferrandez,  J., M.\n</span>\n\n  \n\n</span>\n\n  <i>Neurocomputing</i>, 410: 237-270.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/abs/pii/S0925231220309292?via%3Dihub\"\n     onclick=\"log_download('gorriz-ramirez-ortiz-martinezmurcia-segovia-suckling-leming-zhang-etal-artificialintelligencewithintheinterplaybetweennaturalandartificialcomputationadvancesindatasciencetrendsandapplications-2020', 'https://www.sciencedirect.com/science/article/abs/pii/S0925231220309292?via%3Dihub', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gorriz-ramirez-ortiz-martinezmurcia-segovia-suckling-leming-zhang-etal-artificialintelligencewithintheinterplaybetweennaturalandartificialcomputationadvancesindatasciencetrendsandapplications-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gorriz-ramirez-ortiz-martinezmurcia-segovia-suckling-leming-zhang-etal-artificialintelligencewithintheinterplaybetweennaturalandartificialcomputationadvancesindatasciencetrendsandapplications-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications},\n type = {article},\n year = {2020},\n pages = {237-270},\n volume = {410},\n websites = {https://www.sciencedirect.com/science/article/abs/pii/S0925231220309292?via%3Dihub},\n id = {48a29a5b-f09b-3ffc-ab3f-17558dc46ef8},\n created = {2020-05-27T09:42:19.096Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-08-25T16:21:03.340Z},\n read = {true},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Gorriz, Juan M and Ramirez, Javier and Ortiz, Andres and Martinez-Murcia, Francisco J and Segovia, Fermin and Suckling, John and Leming, Matthew and Zhang, Yu-Dong and Alvarez-Sanchez, Jose Ramon and Bologna, Guido and Bonomini, Paula and Casado, Fernando E and Charte, David and Charte, Francisco and Contreras, Ricardo and Cuesta-Infante, Alfredo and Duro, Richard J and Fernandez-Caballero, Antonio and Fernandez-Jover, Eduardo and Gomez-Vilda, Pedro and Grana, Manuel and Herrera, Francisco and Iglesias, Roberto and Lekova, Anna and de Lope, Javier and Lopez-Rubio, Ezequiel and Martinez Tomas, Rafael and Molina-Cabello, Miguel A and Montemayor, Antonio S and Novais, Paulo and Palacios-Alonso, Daniel and Pantrigo, Juaj J and Payne, Bryson R and de la Paz Lopez, Felix and Angelica Pinninghoff, Maria and Rincon, Mariano and Sanstos, Jose and Thurnhofer-Hemsi, Karl and Tsanas, Athanasios and Varela, Ramiro and Ferrandez, Jose M},\n journal = {Neurocomputing}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tibble-flook-sheikh-tsanas-horne-geest-stagg-measuringandreportingtreatmentadherencewhatcanwelearnbycomparingtworespiratoryconditions-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Measuring and reporting treatment adherence: what can we learn by comparing two respiratory conditions?.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Tibble,  H.; Flook,  M.; Sheikh,  A.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Horne,  R.; Geest,  S., D.;  and Stagg,  H., R.\n</span>\n\n  \n\n</span>\n\n  <i>British Journal of Clinical Pharmacology</i>, (in press).  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tibble-flook-sheikh-tsanas-horne-geest-stagg-measuringandreportingtreatmentadherencewhatcanwelearnbycomparingtworespiratoryconditions-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tibble-flook-sheikh-tsanas-horne-geest-stagg-measuringandreportingtreatmentadherencewhatcanwelearnbycomparingtworespiratoryconditions-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Measuring and reporting treatment adherence: what can we learn by comparing two respiratory conditions?},\n type = {article},\n year = {2020},\n volume = {(in press)},\n id = {8c638d81-90ef-33f5-885a-6377df82ccc9},\n created = {2020-06-25T21:09:09.432Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-06-25T21:14:15.735Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Tibble, Holly and Flook, Mary and Sheikh, Aziz and Tsanas, Athanasios and Horne, Rob and Geest, Sabina De and Stagg, Helen R},\n journal = {British Journal of Clinical Pharmacology}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"dean-ginnell-ledsham-tsanas-telford-sparrow-fletcherwatson-boardman-eyetrackingforlongitudinalassessmentofsocialcognitioninchildrenbornpreterm-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Eye-tracking for longitudinal assessment of social cognition in children born preterm.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Dean,  B.; Ginnell,  L.; Ledsham,  V.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Telford,  E.; Sparrow,  S.; Fletcher-Watson,  S.;  and Boardman,  J.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Child Psychology and Psychiatry</i>, (in press).  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dean-ginnell-ledsham-tsanas-telford-sparrow-fletcherwatson-boardman-eyetrackingforlongitudinalassessmentofsocialcognitioninchildrenbornpreterm-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dean-ginnell-ledsham-tsanas-telford-sparrow-fletcherwatson-boardman-eyetrackingforlongitudinalassessmentofsocialcognitioninchildrenbornpreterm-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Eye-tracking for longitudinal assessment of social cognition in children born preterm},\n type = {article},\n year = {2020},\n keywords = {digestibility,duodenal flow,milk responses,model evaluation},\n volume = {(in press)},\n id = {82fc8a43-2d17-3700-94fc-2021994c0d1a},\n created = {2020-06-25T21:09:09.452Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-06-25T21:11:25.477Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Dean, Bethan and Ginnell, Lorna and Ledsham, Victoria and Tsanas, Athanasios and Telford, Emma and Sparrow, Sarah and Fletcher-Watson, Sue and Boardman, James},\n journal = {Journal of Child Psychology and Psychiatry}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wang-tsanas-blin-robertson-predictingpatternformationinembryonicstemcellsusingaminimalistagentbasedprobabilisticmodel-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1038/s41598-020-73228-4\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wang-tsanas-blin-robertson-predictingpatternformationinembryonicstemcellsusingaminimalistagentbasedprobabilisticmodel-2020', 'https://doi.org/10.1038/s41598-020-73228-4', event);\">\n    Predicting pattern formation in embryonic stem cells using a minimalist, agent‑based probabilistic model.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang,  M.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Blin,  G.;  and Robertson,  D.\n</span>\n\n  \n\n</span>\n\n  <i>Scientific Reports</i>, 10: 16209.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1038/s41598-020-73228-4\"\n     onclick=\"log_download('wang-tsanas-blin-robertson-predictingpatternformationinembryonicstemcellsusingaminimalistagentbasedprobabilisticmodel-2020', 'https://doi.org/10.1038/s41598-020-73228-4', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Predicting pattern formation in embryonic stem cells using a minimalist, agent‑based probabilistic model [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1038/s41598-020-73228-4\"\n     onclick=\"log_download('wang-tsanas-blin-robertson-predictingpatternformationinembryonicstemcellsusingaminimalistagentbasedprobabilisticmodel-2020', 'http://doi.org/10.1038/s41598-020-73228-4', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-tsanas-blin-robertson-predictingpatternformationinembryonicstemcellsusingaminimalistagentbasedprobabilisticmodel-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-tsanas-blin-robertson-predictingpatternformationinembryonicstemcellsusingaminimalistagentbasedprobabilisticmodel-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Predicting pattern formation in embryonic stem cells using a minimalist, agent‑based probabilistic model},\n type = {article},\n year = {2020},\n pages = {16209},\n volume = {10},\n websites = {https://doi.org/10.1038/s41598-020-73228-4},\n publisher = {Nature Publishing Group UK},\n id = {5c8984b1-aba9-3305-8340-d8a992ff00f5},\n created = {2020-10-02T20:06:54.122Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-11-28T13:57:12.835Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},\n doi = {10.1038/s41598-020-73228-4},\n journal = {Scientific Reports}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tibble-chan-mitchell-horne-doudesis-horne-mizani-sheikh-etal-adatadriventypologyofasthmamedicationadherenceusingclusteranalysis-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1038/s41598-020-72060-0\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'tibble-chan-mitchell-horne-doudesis-horne-mizani-sheikh-etal-adatadriventypologyofasthmamedicationadherenceusingclusteranalysis-2020', 'https://doi.org/10.1038/s41598-020-72060-0', event);\">\n    A data-driven typology of asthma medication adherence using cluster analysis.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Tibble,  H.; Chan,  A.; Mitchell,  E., A.; Horne,  E.; Doudesis,  D.; Horne,  R.; Mizani,  M., A.; Sheikh,  A.;  and <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  <i>Scientific Reports</i>, 10: 14999.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1038/s41598-020-72060-0\"\n     onclick=\"log_download('tibble-chan-mitchell-horne-doudesis-horne-mizani-sheikh-etal-adatadriventypologyofasthmamedicationadherenceusingclusteranalysis-2020', 'https://doi.org/10.1038/s41598-020-72060-0', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A data-driven typology of asthma medication adherence using cluster analysis [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1038/s41598-020-72060-0\"\n     onclick=\"log_download('tibble-chan-mitchell-horne-doudesis-horne-mizani-sheikh-etal-adatadriventypologyofasthmamedicationadherenceusingclusteranalysis-2020', 'http://doi.org/10.1038/s41598-020-72060-0', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tibble-chan-mitchell-horne-doudesis-horne-mizani-sheikh-etal-adatadriventypologyofasthmamedicationadherenceusingclusteranalysis-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tibble-chan-mitchell-horne-doudesis-horne-mizani-sheikh-etal-adatadriventypologyofasthmamedicationadherenceusingclusteranalysis-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A data-driven typology of asthma medication adherence using cluster analysis},\n type = {article},\n year = {2020},\n pages = {14999},\n volume = {10},\n websites = {https://doi.org/10.1038/s41598-020-72060-0},\n publisher = {Nature Publishing Group UK},\n id = {55101ab6-04cd-3cba-8893-8dc83be32a1e},\n created = {2020-10-02T20:07:55.135Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-10-02T20:07:55.135Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).},\n bibtype = {article},\n author = {Tibble, Holly and Chan, Amy and Mitchell, Edwin A. and Horne, Elsie and Doudesis, Dimitrios and Horne, Rob and Mizani, Mehrdad A. and Sheikh, Aziz and Tsanas, Athanasios},\n doi = {10.1038/s41598-020-72060-0},\n journal = {Scientific Reports}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tibble-layflurrie-sheikh-horne-mizani-tsanas-linkageofprimarycareprescribingrecordsandpharmacydispensingrecordsinasthmacontrollermedications-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Linkage of Primary Care Prescribing Records and Pharmacy Dispensing Records in Asthma Controller Medications.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Tibble,  H.; Lay-Flurrie,  J.; Sheikh,  A.; Horne,  R.; Mizani,  M.;  and <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  <i>BMC Medical Research Methodology</i>, 11: (in press).  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.23889/ijpds.v4i3.1191\"\n     onclick=\"log_download('tibble-layflurrie-sheikh-horne-mizani-tsanas-linkageofprimarycareprescribingrecordsandpharmacydispensingrecordsinasthmacontrollermedications-2020', 'http://doi.org/10.23889/ijpds.v4i3.1191', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tibble-layflurrie-sheikh-horne-mizani-tsanas-linkageofprimarycareprescribingrecordsandpharmacydispensingrecordsinasthmacontrollermedications-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tibble-layflurrie-sheikh-horne-mizani-tsanas-linkageofprimarycareprescribingrecordsandpharmacydispensingrecordsinasthmacontrollermedications-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Linkage of Primary Care Prescribing Records and Pharmacy Dispensing Records in Asthma Controller Medications},\n type = {article},\n year = {2020},\n pages = {(in press)},\n volume = {11},\n id = {64cb8abe-6c00-350d-a166-3c90fe376174},\n created = {2020-11-27T18:48:57.930Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-11-27T18:48:57.930Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Background In the UK, issued prescriptions are typically taken to pharmacies, where medications are prepared, recorded, and dispensed. Data Linkage between prescribing and pharmacy dispensing records is not routinely conducted at the individual prescription level for clinical care in England and Wales, however it can be particularly useful for the study of pharmacoepidemiology. With no unique prescribing event identifiers between records, an algorithmic approach is required for this linkage. Aims To create a linkage system for primary care prescribed asthma controller medications and pharmacy dispensing records. Methods Free text labels were used to populate fields for data linkage, relating to medication strength, medication type (active ingredients; allows matching of generic substitutions to named brands), doses per medication unit, prescribed units, and prescribed doses. Prescribing and dispensing records were merged using an inner (many to many) join; generating a candidate link for every combination of records matching on unique patient identifier and medicine. A recursive algorithm was developed and applied, working backwards chronologically through dispensing records and finding the most appropriate match based on the time since prescribing and agreement between the medication description fields. Unmatched records were assessed for quality assurance, and the distribution of linkage strength for matches was examined. Results We developed a harmonisation algorithm in a dataset of over 3 million asthma controller medication prescription records, for which almost 3 in 4 were coded according to the number of units (predominantly inhalers). Incorporating the estimated number of doses prescribed/dispensed into our wider matching algorithm, we were able to find unique prescription records for almost 95% of our dispensing records. Conclusion Early findings demonstrate the accuracy of the developed algorithm linking prescribing and dispensing records. This algorithm can easily be generalised to other conditions.},\n bibtype = {article},\n author = {Tibble, Holly and Lay-Flurrie, James and Sheikh, Aziz and Horne, Robert and Mizani, Mehrdad and Tsanas, Athanasios},\n doi = {10.23889/ijpds.v4i3.1191},\n journal = {BMC Medical Research Methodology}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Background In the UK, issued prescriptions are typically taken to pharmacies, where medications are prepared, recorded, and dispensed. Data Linkage between prescribing and pharmacy dispensing records is not routinely conducted at the individual prescription level for clinical care in England and Wales, however it can be particularly useful for the study of pharmacoepidemiology. With no unique prescribing event identifiers between records, an algorithmic approach is required for this linkage. Aims To create a linkage system for primary care prescribed asthma controller medications and pharmacy dispensing records. Methods Free text labels were used to populate fields for data linkage, relating to medication strength, medication type (active ingredients; allows matching of generic substitutions to named brands), doses per medication unit, prescribed units, and prescribed doses. Prescribing and dispensing records were merged using an inner (many to many) join; generating a candidate link for every combination of records matching on unique patient identifier and medicine. A recursive algorithm was developed and applied, working backwards chronologically through dispensing records and finding the most appropriate match based on the time since prescribing and agreement between the medication description fields. Unmatched records were assessed for quality assurance, and the distribution of linkage strength for matches was examined. Results We developed a harmonisation algorithm in a dataset of over 3 million asthma controller medication prescription records, for which almost 3 in 4 were coded according to the number of units (predominantly inhalers). Incorporating the estimated number of doses prescribed/dispensed into our wider matching algorithm, we were able to find unique prescription records for almost 95% of our dispensing records. Conclusion Early findings demonstrate the accuracy of the developed algorithm linking prescribing and dispensing records. This algorithm can easily be generalised to other conditions.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"woodward-kanjo-brown-mcginnity-inkster-macintyre-tsanas-beyondmobileappsasurveyoftechnologiesformentalwellbeing-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Beyond mobile apps: A survey of technologies for mental well-being.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Woodward,  K.; Kanjo,  E.; Brown,  D.; McGinnity,  T.; Inkster,  B.; Macintyre,  D.;  and <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Transactions on Affective Computing</i>,(in press).  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/woodward-kanjo-brown-mcginnity-inkster-macintyre-tsanas-beyondmobileappsasurveyoftechnologiesformentalwellbeing-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('woodward-kanjo-brown-mcginnity-inkster-macintyre-tsanas-beyondmobileappsasurveyoftechnologiesformentalwellbeing-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Beyond mobile apps: A survey of technologies for mental well-being},\n type = {article},\n year = {2020},\n keywords = {Diagnosis or assessment,Machine learning,Pervasive computing,Physiological Measures,Ubiquitous computing},\n pages = {(in press)},\n id = {19b40068-6b54-33b1-8188-27d8661a1c21},\n created = {2020-11-27T18:53:31.231Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-11-27T18:53:31.231Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the development of a robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.},\n bibtype = {article},\n author = {Woodward, K. and Kanjo, E. and Brown, D. and McGinnity, T.M. and Inkster, B. and Macintyre, D.J. and Tsanas, A.},\n journal = {IEEE Transactions on Affective Computing}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the development of a robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (11)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"vogel-tsanas-scattoni-quantifyingultrasonicmousevocalizationsusingacousticanalysisinasueprvisedstatisticalmachinelearningframework-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Quantifying ultrasonic mouse vocalizations using acoustic analysis in a sueprvised statistical machine learning framework.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Vogel,  A.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>;  and Scattoni,  M., L.\n</span>\n\n  \n\n</span>\n\n  <i>Scientific Reports</i>, 9(1): 8100.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vogel-tsanas-scattoni-quantifyingultrasonicmousevocalizationsusingacousticanalysisinasueprvisedstatisticalmachinelearningframework-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('vogel-tsanas-scattoni-quantifyingultrasonicmousevocalizationsusingacousticanalysisinasueprvisedstatisticalmachinelearningframework-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Quantifying ultrasonic mouse vocalizations using acoustic analysis in a sueprvised statistical machine learning framework},\n type = {article},\n year = {2019},\n pages = {8100},\n volume = {9},\n id = {80ec7b1e-c8a2-38d1-974d-98fc12780f15},\n created = {2019-05-14T23:34:11.757Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2019-06-04T19:05:30.909Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Vogel, Adam and Tsanas, Athanasios and Scattoni, Maria Luisa},\n journal = {Scientific Reports},\n number = {1}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tibble-tsanas-horne-horne-mizani-simpson-sheikh-predictingasthmaattacksinprimarycareprotocolfordevelopingamachinelearningbasedpredictionmodel-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Tibble,  H.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Horne,  E.; Horne,  R.; Mizani,  M., A.; Simpson,  C., R.;  and Sheikh,  A.\n</span>\n\n  \n\n</span>\n\n  <i>BMJ Open</i>, 9(7): e028375.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tibble-tsanas-horne-horne-mizani-simpson-sheikh-predictingasthmaattacksinprimarycareprotocolfordevelopingamachinelearningbasedpredictionmodel-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tibble-tsanas-horne-horne-mizani-simpson-sheikh-predictingasthmaattacksinprimarycareprotocolfordevelopingamachinelearningbasedpredictionmodel-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model},\n type = {article},\n year = {2019},\n pages = {e028375},\n volume = {9},\n id = {4d28f322-204f-3f22-abf1-28fee9656249},\n created = {2019-06-04T19:05:30.760Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-02-17T22:34:58.508Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Tibble, Holly and Tsanas, Athanasios and Horne, Elsie and Horne, Robert and Mizani, Mehrdad A and Simpson, Colin R and Sheikh, Aziz},\n journal = {BMJ Open},\n number = {7}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"than-pickering-sandoval-shah-tsanas-apple-blankenberg-cullen-etal-machinelearningtopredictthelikelihoodofacutemyocardialinfarction-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.ncbi.nlm.nih.gov/pubmed/31416346\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'than-pickering-sandoval-shah-tsanas-apple-blankenberg-cullen-etal-machinelearningtopredictthelikelihoodofacutemyocardialinfarction-2019', 'http://www.ncbi.nlm.nih.gov/pubmed/31416346', event);\">\n    Machine Learning to Predict the Likelihood of Acute Myocardial Infarction.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Than,  M., P.; Pickering,  J., W.; Sandoval,  Y.; Shah,  A., S., V.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Apple,  F., S.; Blankenberg,  S.; Cullen,  L.; Mueller,  C.; Neumann,  J., T.; Twerenbold,  R.; Westermann,  D.; Beshiri,  A.; Mills,  N., L.;  and MI3 collaborative\n</span>\n\n  \n\n</span>\n\n  <i>Circulation</i>, 140: 899-909.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.ncbi.nlm.nih.gov/pubmed/31416346\"\n     onclick=\"log_download('than-pickering-sandoval-shah-tsanas-apple-blankenberg-cullen-etal-machinelearningtopredictthelikelihoodofacutemyocardialinfarction-2019', 'http://www.ncbi.nlm.nih.gov/pubmed/31416346', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Machine Learning to Predict the Likelihood of Acute Myocardial Infarction [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1161/CIRCULATIONAHA.119.041980\"\n     onclick=\"log_download('than-pickering-sandoval-shah-tsanas-apple-blankenberg-cullen-etal-machinelearningtopredictthelikelihoodofacutemyocardialinfarction-2019', 'http://doi.org/10.1161/CIRCULATIONAHA.119.041980', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/than-pickering-sandoval-shah-tsanas-apple-blankenberg-cullen-etal-machinelearningtopredictthelikelihoodofacutemyocardialinfarction-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('than-pickering-sandoval-shah-tsanas-apple-blankenberg-cullen-etal-machinelearningtopredictthelikelihoodofacutemyocardialinfarction-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Machine Learning to Predict the Likelihood of Acute Myocardial Infarction},\n type = {article},\n year = {2019},\n keywords = {acute coronary syndrome,infarction,machine learning,myocardial,see page 908,sources of funding,troponin},\n pages = {899-909},\n volume = {140},\n websites = {http://www.ncbi.nlm.nih.gov/pubmed/31416346},\n id = {7dfa3078-91b6-31de-8b9d-12fe7af21d1d},\n created = {2019-09-15T10:20:10.856Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-02-17T19:39:57.570Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {BACKGROUND Variations in cardiac troponin concentrations by age, sex and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. METHODS A machine learning algorithm (myocardial-ischemic-injury-index [MI3]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3,013 patients and tested on 7,998 patients with suspected myocardial infarction. MI3 uses gradient boosting to compute a value (0-100) reflecting an individual&#x27;s likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value (NPV), specificity and positive predictive value (PPV) for that individual. Assessment was by calibration and area under the receiver-operating-characteristic curve (AUC). Secondary analysis evaluated example MI3 thresholds from the training set that identified patients as low-risk (99% sensitivity) and high-risk (75% PPV), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology (ESC) rule-out pathways. RESULTS Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI3 was well calibrated with a very high AUC of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI3 thresholds identifying low-risk and high-risk patients in the training set were 1.6 and 49.7 respectively. In the test set, MI3 values were &lt;1.6 in 69.5% with a NPV of 99.7% (99.5%-99.8%) and sensitivity of 97.8% (96.7-98.7%), and were ≥49.7 in 10.6% with a PPV of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI3 performed better than the ESC 0/3-hour pathway (sensitivity 82.5% [74.5-88.8%], specificity 92.2% [90.7-93.5%]) and the 99th percentile at any time-point (sensitivity 89.6% [87.4-91.6%]), specificity 89.3% [88.6-90.0%]). CONCLUSIONS Using machine learning, MI3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low-risk and high-risk patients who may benefit from earlier clinical decisions. CLINICAL TRIAL REGISTRATION Unique Identifier: Australian New Zealand Clinical Trials Registry: ACTRN12616001441404. URL: https://www.anzctr.org.au.},\n bibtype = {article},\n author = {Than, Martin P and Pickering, John W and Sandoval, Yader and Shah, Anoop S V and Tsanas, Athanasios and Apple, Fred S and Blankenberg, Stefan and Cullen, Louise and Mueller, Christian and Neumann, Johannes T and Twerenbold, Raphael and Westermann, Dirk and Beshiri, Agim and Mills, Nicholas L and MI3 collaborative, undefined},\n doi = {10.1161/CIRCULATIONAHA.119.041980},\n journal = {Circulation}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  BACKGROUND Variations in cardiac troponin concentrations by age, sex and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. METHODS A machine learning algorithm (myocardial-ischemic-injury-index [MI3]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3,013 patients and tested on 7,998 patients with suspected myocardial infarction. MI3 uses gradient boosting to compute a value (0-100) reflecting an individual's likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value (NPV), specificity and positive predictive value (PPV) for that individual. Assessment was by calibration and area under the receiver-operating-characteristic curve (AUC). Secondary analysis evaluated example MI3 thresholds from the training set that identified patients as low-risk (99% sensitivity) and high-risk (75% PPV), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology (ESC) rule-out pathways. RESULTS Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI3 was well calibrated with a very high AUC of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI3 thresholds identifying low-risk and high-risk patients in the training set were 1.6 and 49.7 respectively. In the test set, MI3 values were <1.6 in 69.5% with a NPV of 99.7% (99.5%-99.8%) and sensitivity of 97.8% (96.7-98.7%), and were ≥49.7 in 10.6% with a PPV of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI3 performed better than the ESC 0/3-hour pathway (sensitivity 82.5% [74.5-88.8%], specificity 92.2% [90.7-93.5%]) and the 99th percentile at any time-point (sensitivity 89.6% [87.4-91.6%]), specificity 89.3% [88.6-90.0%]). CONCLUSIONS Using machine learning, MI3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low-risk and high-risk patients who may benefit from earlier clinical decisions. CLINICAL TRIAL REGISTRATION Unique Identifier: Australian New Zealand Clinical Trials Registry: ACTRN12616001441404. URL: https://www.anzctr.org.au.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tsanas-arora-biomedicalspeechsignalinsightsfromalargescalecohortacrosssevencountriestheparkinsonsvoiceinitiativestudy-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Biomedical speech signal insights from a large scale cohort across seven countries: the Parkinson's voice initiative study.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>;  and Arora,  S.\n</span>\n\n  \n\n</span>\n\n  In <i>11th International Workshop Models and Analysis of Vocal Emissiong for Biomedical Applications (MAVEBA)</i>, pages 45-48,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsanas-arora-biomedicalspeechsignalinsightsfromalargescalecohortacrosssevencountriestheparkinsonsvoiceinitiativestudy-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsanas-arora-biomedicalspeechsignalinsightsfromalargescalecohortacrosssevencountriestheparkinsonsvoiceinitiativestudy-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Biomedical speech signal insights from a large scale cohort across seven countries: the Parkinson&#x27;s voice initiative study},\n type = {inproceedings},\n year = {2019},\n keywords = {parkinson,pd,pvi,s,s disease,speech signal processing,sustained vowel phonations,voice initiative},\n pages = {45-48},\n city = {Florence, Italy},\n id = {8d91e9a7-2a45-3156-b622-40eb83ca9d5d},\n created = {2019-09-15T10:36:53.386Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-02-12T10:49:19.428Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Tsanas, Athanasios and Arora, Siddharth},\n booktitle = {11th International Workshop Models and Analysis of Vocal Emissiong for Biomedical Applications (MAVEBA)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tsanas-newinsightsintoparkinsonsdiseasethroughstatisticalanalysisofstandardclinicalscalesquantifyingsymptomseverity-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>41st IEEE Engineering in Medicine and Biology Conference</i>, pages 3412-3415,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsanas-newinsightsintoparkinsonsdiseasethroughstatisticalanalysisofstandardclinicalscalesquantifyingsymptomseverity-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsanas-newinsightsintoparkinsonsdiseasethroughstatisticalanalysisofstandardclinicalscalesquantifyingsymptomseverity-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity},\n type = {inproceedings},\n year = {2019},\n pages = {3412-3415},\n city = {Berlin, Germany},\n id = {71341c45-b5f6-3384-9cd9-d8ecc00e832d},\n created = {2019-09-15T10:36:53.439Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-02-17T22:34:58.507Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Tsanas, Athanasios},\n booktitle = {41st IEEE Engineering in Medicine and Biology Conference}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"palaciosalonso-lzarocarrascosa-lpezarribas-melndezmorales-gmezrodellar-lorolavez-nietolluis-rodellarbiarge-etal-understandingthebrainfunctionandemotionsiwinac2019lecturenotesincomputerscienceseries-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Assessing an application of spontaneous stressed speech - emotions portal.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Palacios-Alonso,  D.; Lázaro-Carrascosa,  C.; López-Arribas,  A.; Meléndez-Morales,  G.; Gómez-Rodellar,  A.; Loro-Álavez,  A.; Nieto-Lluis,  V.; Rodellar-Biarge,  V.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>;  and Gómez-Vilda,  P.\n</span>\n\n  \n\n</span>\n\n  Volume 1148 . Understanding the Brain Function and Emotions. IWINAC 2019 (Lecture Notes in Computer Science series), pages 149-160. Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J.,  A., H., editor(s).  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-19591-5_16\"\n     onclick=\"log_download('palaciosalonso-lzarocarrascosa-lpezarribas-melndezmorales-gmezrodellar-lorolavez-nietolluis-rodellarbiarge-etal-understandingthebrainfunctionandemotionsiwinac2019lecturenotesincomputerscienceseries-2019', 'http://doi.org/10.1007/978-3-030-19591-5_16', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/palaciosalonso-lzarocarrascosa-lpezarribas-melndezmorales-gmezrodellar-lorolavez-nietolluis-rodellarbiarge-etal-understandingthebrainfunctionandemotionsiwinac2019lecturenotesincomputerscienceseries-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('palaciosalonso-lzarocarrascosa-lpezarribas-melndezmorales-gmezrodellar-lorolavez-nietolluis-rodellarbiarge-etal-understandingthebrainfunctionandemotionsiwinac2019lecturenotesincomputerscienceseries-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2019},\n keywords = {Characterizing stress,Cooperative framework,Data acquisition,Emotional stress,Stress behavior in human-computer interaction},\n pages = {149-160},\n volume = {1148},\n id = {e935baeb-8205-3129-9e87-61515e9542e8},\n created = {2019-09-15T11:14:59.627Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2019-09-15T11:21:21.359Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inbook},\n author = {Palacios-Alonso, Daniel and Lázaro-Carrascosa, Carlos and López-Arribas, Agustín and Meléndez-Morales, Guillermo and Gómez-Rodellar, Andrés and Loro-Álavez, Andrés and Nieto-Lluis, Victor and Rodellar-Biarge, Victoria and Tsanas, Athanasios and Gómez-Vilda, Pedro},\n editor = {Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H.},\n doi = {10.1007/978-3-030-19591-5_16},\n chapter = {Assessing an application of spontaneous stressed speech - emotions portal},\n title = {Understanding the Brain Function and Emotions. IWINAC 2019 (Lecture Notes in Computer Science series)}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tsanas-arora-exploringtelephonequalityspeechsignalstowardsparkinsonsdiseaseassessmentinalargeacousticallynoncontrolledstudy-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Exploring telephone-quality speech signals towards parkinson's disease assessment in a large acoustically non-controlled study.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>;  and Arora,  S.\n</span>\n\n  \n\n</span>\n\n  In <i>19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)</i>, pages 953-956,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/BIBE.2019.00178\"\n     onclick=\"log_download('tsanas-arora-exploringtelephonequalityspeechsignalstowardsparkinsonsdiseaseassessmentinalargeacousticallynoncontrolledstudy-2019', 'http://doi.org/10.1109/BIBE.2019.00178', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsanas-arora-exploringtelephonequalityspeechsignalstowardsparkinsonsdiseaseassessmentinalargeacousticallynoncontrolledstudy-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsanas-arora-exploringtelephonequalityspeechsignalstowardsparkinsonsdiseaseassessmentinalargeacousticallynoncontrolledstudy-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Exploring telephone-quality speech signals towards parkinson&#x27;s disease assessment in a large acoustically non-controlled study},\n type = {inproceedings},\n year = {2019},\n keywords = {Data visualization,Dimensionality reduction,Parkinson&#x27;s Disease (PD),Sustained vowels},\n pages = {953-956},\n id = {efdb297f-c825-3b67-8897-abc9510fc296},\n created = {2020-02-12T10:49:19.200Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-05-08T17:19:02.659Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The use of high-quality speech signals has led to considerable breakthroughs in Parkinson&#x27;s Disease (PD) in the last decade. These include accurate differentiation of PD versus Healthy Controls (HC) and monitoring longitudinal PD symptom severity. We recently concluded the Parkinson&#x27;s Voice Initiative (PVI) study collecting data from a very large cohort under non-controlled acoustic conditions. We acoustically characterized 11,942 recordings from 6531 US-based participants using 307 dysphonia measures. We selected a robust subset of 30 dysphonia measures using Gram-Schmidt Orthogonalization (GSO). We projected the data onto a two-dimensional representation using t-distributed stochastic neighbor embedding to facilitate visual exploration, and used hierarchical clustering to understand data homogeneity. We demonstrate that there is considerable overlap in the projected feature space between PD and HC, making the binary classification task particularly challenging. The data was grouped into nine clusters using hierarchical clustering which was in broad agreement with the projected two-dimensional representation. These results provide some new insights into understanding the new challenges posed in the PVI project where acoustic recordings conditions were not controlled.},\n bibtype = {inproceedings},\n author = {Tsanas, Athanasios and Arora, Siddharth},\n doi = {10.1109/BIBE.2019.00178},\n booktitle = {19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The use of high-quality speech signals has led to considerable breakthroughs in Parkinson's Disease (PD) in the last decade. These include accurate differentiation of PD versus Healthy Controls (HC) and monitoring longitudinal PD symptom severity. We recently concluded the Parkinson's Voice Initiative (PVI) study collecting data from a very large cohort under non-controlled acoustic conditions. We acoustically characterized 11,942 recordings from 6531 US-based participants using 307 dysphonia measures. We selected a robust subset of 30 dysphonia measures using Gram-Schmidt Orthogonalization (GSO). We projected the data onto a two-dimensional representation using t-distributed stochastic neighbor embedding to facilitate visual exploration, and used hierarchical clustering to understand data homogeneity. We demonstrate that there is considerable overlap in the projected feature space between PD and HC, making the binary classification task particularly challenging. The data was grouped into nine clusters using hierarchical clustering which was in broad agreement with the projected two-dimensional representation. These results provide some new insights into understanding the new challenges posed in the PVI project where acoustic recordings conditions were not controlled.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wang-tsanas-blin-robertson-investigatingmotilityandpatternformationinpluripotentstemcellsthroughagentbasedmodeling-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Investigating motility and pattern formation in pluripotent stem cells through agent-based modeling.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang,  M.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Blin,  G.;  and Robertson,  D.\n</span>\n\n  \n\n</span>\n\n  In <i>19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)</i>, pages 909-913,  2019. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/BIBE.2019.00170\"\n     onclick=\"log_download('wang-tsanas-blin-robertson-investigatingmotilityandpatternformationinpluripotentstemcellsthroughagentbasedmodeling-2019', 'http://doi.org/10.1109/BIBE.2019.00170', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-tsanas-blin-robertson-investigatingmotilityandpatternformationinpluripotentstemcellsthroughagentbasedmodeling-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-tsanas-blin-robertson-investigatingmotilityandpatternformationinpluripotentstemcellsthroughagentbasedmodeling-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Investigating motility and pattern formation in pluripotent stem cells through agent-based modeling},\n type = {inproceedings},\n year = {2019},\n keywords = {Agent-based modelling,Pattern formation,Pluripotent stem cells},\n pages = {909-913},\n publisher = {IEEE},\n city = {Athens, Greece},\n id = {1451e661-713b-37e7-8658-a48ec10384d1},\n created = {2020-02-17T22:00:12.274Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-05-08T17:19:02.721Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Understanding and predicting the pattern formation in groups of pluripotent stem cells has the potential to improve efficiency and efficacy of stem cell therapies. However, the underlying molecular mechanisms of pluripotent stem cell behaviors are highly complex and are currently still not fully understood. A key practical question is whether deep biological modelling of the cells is essential to predict their pattern formation, or whether there is sufficient predictive power in simply modelling their behaviors and interactions at a higher level. This study focuses on the social interactions and behaviors of pluripotent stem cells at a high-level to predict aggregate crowd behaviors within a level of uncertainty. Agent-based modelling was applied to study the pattern formation in pluripotent stem cells. Five models were established to test four biologically plausible rules of cell motility in terms of: a) velocity, b) directional persistence time, c) directional movements, and d) border effect. We found that it is possible that cells&#x27; directional movements based on local density play an important role of the pattern formation, and pattern formation in pluripotent stem cells is governed by a complex combination of rules in our agent-based model simulations, which account for much of the variability observed in experimental findings.},\n bibtype = {inproceedings},\n author = {Wang, Minhong and Tsanas, Athanasios and Blin, Guillaume and Robertson, Dave},\n doi = {10.1109/BIBE.2019.00170},\n booktitle = {19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Understanding and predicting the pattern formation in groups of pluripotent stem cells has the potential to improve efficiency and efficacy of stem cell therapies. However, the underlying molecular mechanisms of pluripotent stem cell behaviors are highly complex and are currently still not fully understood. A key practical question is whether deep biological modelling of the cells is essential to predict their pattern formation, or whether there is sufficient predictive power in simply modelling their behaviors and interactions at a higher level. This study focuses on the social interactions and behaviors of pluripotent stem cells at a high-level to predict aggregate crowd behaviors within a level of uncertainty. Agent-based modelling was applied to study the pattern formation in pluripotent stem cells. Five models were established to test four biologically plausible rules of cell motility in terms of: a) velocity, b) directional persistence time, c) directional movements, and d) border effect. We found that it is possible that cells' directional movements based on local density play an important role of the pattern formation, and pattern formation in pluripotent stem cells is governed by a complex combination of rules in our agent-based model simulations, which account for much of the variability observed in experimental findings.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tibble-chan-mitchell-horne-mizani-sheikh-tsanas-heterogeneityinasthmamedicationadherencemeasurement-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Heterogeneity in asthma medication adherence measurement.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Tibble,  H.; Chan,  A.; Mitchell,  E., A.; Horne,  R.; Mizani,  M., A.; Sheikh,  A.;  and <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)</i>, pages 899-903,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/BIBE.2019.00168\"\n     onclick=\"log_download('tibble-chan-mitchell-horne-mizani-sheikh-tsanas-heterogeneityinasthmamedicationadherencemeasurement-2019', 'http://doi.org/10.1109/BIBE.2019.00168', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tibble-chan-mitchell-horne-mizani-sheikh-tsanas-heterogeneityinasthmamedicationadherencemeasurement-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tibble-chan-mitchell-horne-mizani-sheikh-tsanas-heterogeneityinasthmamedicationadherencemeasurement-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Heterogeneity in asthma medication adherence measurement},\n type = {inproceedings},\n year = {2019},\n keywords = {Adherence,Asthma,Electronic monitoring,Medication,Pediatric},\n pages = {899-903},\n city = {Athens, Greece},\n id = {3b61ab79-336d-3a0d-98a4-b972e1204f95},\n created = {2020-02-17T22:00:12.347Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-05-08T17:19:02.655Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Medication non-adherence is strongly associated with poor asthma control and outcomes. Many studies use an aggregate measure of adherence, such as the percentage of prescribed doses that were taken, however this conceals variation between patients&#x27; medication-taking routines. Electronic monitoring devices, which precisely record the date and time of a dose being actuated from an inhaler, provide the means to objectively and remotely monitor adherence behavior patterns. This secondary analysis of a New Zealand audio-visual medication reminder intervention study visually explored the relationships, variation, and heterogeneity between multiple measures of adherence, in 211 children aged 6-15 years old who presented to an emergency department with an asthma attack. Our findings highlight the weakness of statistical relationships between measures of adherence, and the irregularity in patient medication-taking behavior. This demonstrates that a single aggregate adherence measure fails to detect asthma patients for whom their day-to-day medication taking (implementation) is inconsistent with their longitudinal medication taking (persistence).},\n bibtype = {inproceedings},\n author = {Tibble, Holly and Chan, Amy and Mitchell, Edwin A. and Horne, Rob and Mizani, Mehrdad A. and Sheikh, Aziz and Tsanas, Athanasios},\n doi = {10.1109/BIBE.2019.00168},\n booktitle = {19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Medication non-adherence is strongly associated with poor asthma control and outcomes. Many studies use an aggregate measure of adherence, such as the percentage of prescribed doses that were taken, however this conceals variation between patients' medication-taking routines. Electronic monitoring devices, which precisely record the date and time of a dose being actuated from an inhaler, provide the means to objectively and remotely monitor adherence behavior patterns. This secondary analysis of a New Zealand audio-visual medication reminder intervention study visually explored the relationships, variation, and heterogeneity between multiple measures of adherence, in 211 children aged 6-15 years old who presented to an emergency department with an asthma attack. Our findings highlight the weakness of statistical relationships between measures of adherence, and the irregularity in patient medication-taking behavior. This demonstrates that a single aggregate adherence measure fails to detect asthma patients for whom their day-to-day medication taking (implementation) is inconsistent with their longitudinal medication taking (persistence).\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"arora-baghairavary-tsanas-developingalargescalepopulationscreeningtoolfortheassessmentofparkinsonsdiseaseusingtelephonequalityvoice-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1121/1.5100272\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'arora-baghairavary-tsanas-developingalargescalepopulationscreeningtoolfortheassessmentofparkinsonsdiseaseusingtelephonequalityvoice-2019', 'http://dx.doi.org/10.1121/1.5100272', event);\">\n    Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Arora,  S.; Baghai-Ravary,  L.;  and <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  <i>Journal of the Acoustical Society of America</i>, 145(5): 2871-2884.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1121/1.5100272\"\n     onclick=\"log_download('arora-baghairavary-tsanas-developingalargescalepopulationscreeningtoolfortheassessmentofparkinsonsdiseaseusingtelephonequalityvoice-2019', 'http://dx.doi.org/10.1121/1.5100272', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Developing a large scale population screening tool for the assessment of Parkinson&#x27;s disease using telephone-quality voice [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1121/1.5100272\"\n     onclick=\"log_download('arora-baghairavary-tsanas-developingalargescalepopulationscreeningtoolfortheassessmentofparkinsonsdiseaseusingtelephonequalityvoice-2019', 'http://doi.org/10.1121/1.5100272', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/arora-baghairavary-tsanas-developingalargescalepopulationscreeningtoolfortheassessmentofparkinsonsdiseaseusingtelephonequalityvoice-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('arora-baghairavary-tsanas-developingalargescalepopulationscreeningtoolfortheassessmentofparkinsonsdiseaseusingtelephonequalityvoice-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Developing a large scale population screening tool for the assessment of Parkinson&#x27;s disease using telephone-quality voice},\n type = {article},\n year = {2019},\n pages = {2871-2884},\n volume = {145},\n websites = {http://dx.doi.org/10.1121/1.5100272},\n id = {c1bf6321-9df0-395d-a2e2-b489419875bd},\n created = {2020-04-30T12:14:51.819Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-04-30T12:14:51.819Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson&#x27;s disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.},\n bibtype = {article},\n author = {Arora, Siddharth and Baghai-Ravary, Ladan and Tsanas, Athanasios},\n doi = {10.1121/1.5100272},\n journal = {Journal of the Acoustical Society of America},\n number = {5}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"triantafyllidis-tsanas-applicationsofmachinelearninginreallifedigitalhealthinterventionsreviewoftheliterature-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Applications of machine learning in real-life digital health interventions: Review of the literature.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Triantafyllidis,  A., K.;  and <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Medical Internet Research</i>, 21(4): 1-9.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.2196/12286\"\n     onclick=\"log_download('triantafyllidis-tsanas-applicationsofmachinelearninginreallifedigitalhealthinterventionsreviewoftheliterature-2019', 'http://doi.org/10.2196/12286', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/triantafyllidis-tsanas-applicationsofmachinelearninginreallifedigitalhealthinterventionsreviewoftheliterature-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('triantafyllidis-tsanas-applicationsofmachinelearninginreallifedigitalhealthinterventionsreviewoftheliterature-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Applications of machine learning in real-life digital health interventions: Review of the literature},\n type = {article},\n year = {2019},\n keywords = {Artificial intelligence,Data mining,Digital health,Machine learning,Review,Telemedicine},\n pages = {1-9},\n volume = {21},\n id = {4addf79b-282a-34d9-9174-7f7a07d3686d},\n created = {2020-04-30T12:14:51.821Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2020-04-30T12:14:51.821Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Background: Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals. Objective: Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain. Methods: We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction). Results: Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes. Conclusions: This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.},\n bibtype = {article},\n author = {Triantafyllidis, Andreas K. and Tsanas, Athanasios},\n doi = {10.2196/12286},\n journal = {Journal of Medical Internet Research},\n number = {4}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Background: Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals. Objective: Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain. Methods: We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction). Results: Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes. Conclusions: This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"carr-saunders-tsanas-palmius-geddes-foster-goodwin-devos-variabilityinphaseandamplitudeofdiurnalrhythmsisrelatedtovariationofmoodinbipolarandborderlinepersonalitydisorder-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1038/s41598-018-19888-9\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'carr-saunders-tsanas-palmius-geddes-foster-goodwin-devos-variabilityinphaseandamplitudeofdiurnalrhythmsisrelatedtovariationofmoodinbipolarandborderlinepersonalitydisorder-2018', 'http://dx.doi.org/10.1038/s41598-018-19888-9', event);\">\n    Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Carr,  O.; Saunders,  K., E., A.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Palmius,  N.; Geddes,  J., R.; Foster,  R.; Goodwin,  G., M.;  and De Vos,  M.\n</span>\n\n  \n\n</span>\n\n  <i>Scientific reports</i>, 8: 1649.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1038/s41598-018-19888-9\"\n     onclick=\"log_download('carr-saunders-tsanas-palmius-geddes-foster-goodwin-devos-variabilityinphaseandamplitudeofdiurnalrhythmsisrelatedtovariationofmoodinbipolarandborderlinepersonalitydisorder-2018', 'http://dx.doi.org/10.1038/s41598-018-19888-9', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1038/s41598-018-19888-9\"\n     onclick=\"log_download('carr-saunders-tsanas-palmius-geddes-foster-goodwin-devos-variabilityinphaseandamplitudeofdiurnalrhythmsisrelatedtovariationofmoodinbipolarandborderlinepersonalitydisorder-2018', 'http://doi.org/10.1038/s41598-018-19888-9', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/carr-saunders-tsanas-palmius-geddes-foster-goodwin-devos-variabilityinphaseandamplitudeofdiurnalrhythmsisrelatedtovariationofmoodinbipolarandborderlinepersonalitydisorder-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('carr-saunders-tsanas-palmius-geddes-foster-goodwin-devos-variabilityinphaseandamplitudeofdiurnalrhythmsisrelatedtovariationofmoodinbipolarandborderlinepersonalitydisorder-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder},\n type = {article},\n year = {2018},\n pages = {1649},\n volume = {8},\n websites = {http://dx.doi.org/10.1038/s41598-018-19888-9},\n publisher = {Springer US},\n id = {e30f810e-83d0-3682-905a-bc50e7462ab6},\n created = {2019-02-22T10:25:09.690Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2019-02-22T10:29:38.343Z},\n read = {true},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Carr, Oliver and Saunders, Kate E. A. and Tsanas, Athanasios and Palmius, Niclas and Geddes, John R. and Foster, Russell and Goodwin, Guy M. and De Vos, Maarten},\n doi = {10.1038/s41598-018-19888-9},\n journal = {Scientific reports}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"carr-saunders-bilderbeck-tsanas-palmius-geddes-foster-devos-etal-desynchronizationofdiurnalrhythmsinbipolardisorderandborderlinepersonalitydisorder-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Carr,  O.; Saunders,  K.; Bilderbeck,  A.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Palmius,  N.; Geddes,  J.; Foster,  R.; De Vos,  M.;  and Goodwin,  G.\n</span>\n\n  \n\n</span>\n\n  <i>Translational Psychiatry</i>, 8: 79.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1038/s41398-018-0125-7\"\n     onclick=\"log_download('carr-saunders-bilderbeck-tsanas-palmius-geddes-foster-devos-etal-desynchronizationofdiurnalrhythmsinbipolardisorderandborderlinepersonalitydisorder-2018', 'http://doi.org/10.1038/s41398-018-0125-7', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/carr-saunders-bilderbeck-tsanas-palmius-geddes-foster-devos-etal-desynchronizationofdiurnalrhythmsinbipolardisorderandborderlinepersonalitydisorder-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('carr-saunders-bilderbeck-tsanas-palmius-geddes-foster-devos-etal-desynchronizationofdiurnalrhythmsinbipolardisorderandborderlinepersonalitydisorder-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder},\n type = {article},\n year = {2018},\n pages = {79},\n volume = {8},\n id = {e2876a17-b154-3c03-850d-6524c5e45e2f},\n created = {2019-02-22T10:25:09.736Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2019-02-22T10:29:38.355Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2018 The Author(s). It has long been proposed that diurnal rhythms are disturbed in bipolar disorder (BD). Such changes are obvious in episodes of mania or depression. However, detailed study of patients between episodes has been rare and comparison with other psychiatric disorders rarer still. Our hypothesis was that evidence for desynchronization of diurnal rhythms would be evident in BD and that we could test the specificity of any effect by studying borderline personality disorder (BPD). Individuals with BD (n = 36), BPD (n = 22) and healthy volunteers (HC, n = 25) wore a portable heart rate and actigraphy device and used a smart-phone to record self-assessed mood scores 10 times per day for 1 week. Average diurnal patterns of heart rate (HR), activity and sleep were compared within and across groups. Desynchronization in the phase of diurnal rhythms of HR compared with activity were found in BPD (+3 h) and BD (+1 h), but not in HC. A clear diurnal pattern for positive mood was found in all subject groups. The coherence between negative and irritable mood and HR showed a four-cycle per day component in BD and BPD, which was not present in HC. The findings highlight marked de-synchronisation of measured diurnal function in both BD but particularly BPD and suggest an increased association with negative and irritable mood at ultradian frequencies. These findings enhance our understanding of the underlying physiological changes associated with BPD and BD, and suggest objective markers for monitoring and potential treatment targets. Improved mood stabilisation is a translational objective for management of both patient groups.},\n bibtype = {article},\n author = {Carr, O. and Saunders, K.E.A. and Bilderbeck, A.C. and Tsanas, A. and Palmius, N. and Geddes, J.R. and Foster, R. and De Vos, M. and Goodwin, G.M.},\n doi = {10.1038/s41398-018-0125-7},\n journal = {Translational Psychiatry}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  © 2018 The Author(s). It has long been proposed that diurnal rhythms are disturbed in bipolar disorder (BD). Such changes are obvious in episodes of mania or depression. However, detailed study of patients between episodes has been rare and comparison with other psychiatric disorders rarer still. Our hypothesis was that evidence for desynchronization of diurnal rhythms would be evident in BD and that we could test the specificity of any effect by studying borderline personality disorder (BPD). Individuals with BD (n = 36), BPD (n = 22) and healthy volunteers (HC, n = 25) wore a portable heart rate and actigraphy device and used a smart-phone to record self-assessed mood scores 10 times per day for 1 week. Average diurnal patterns of heart rate (HR), activity and sleep were compared within and across groups. Desynchronization in the phase of diurnal rhythms of HR compared with activity were found in BPD (+3 h) and BD (+1 h), but not in HC. A clear diurnal pattern for positive mood was found in all subject groups. The coherence between negative and irritable mood and HR showed a four-cycle per day component in BD and BPD, which was not present in HC. The findings highlight marked de-synchronisation of measured diurnal function in both BD but particularly BPD and suggest an increased association with negative and irritable mood at ultradian frequencies. These findings enhance our understanding of the underlying physiological changes associated with BPD and BD, and suggest objective markers for monitoring and potential treatment targets. Improved mood stabilisation is a translational objective for management of both patient groups.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"arora-visanji-mestre-tsanas-aldakheel-connolly-gascasalas-kern-etal-investigatingvoiceasabiomarkerforleucinerichrepeatkinase2associatedparkinsonsdisease-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Investigating Voice as a Biomarker for leucine-rich repeat kinase 2-Associated Parkinson’s Disease.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Arora,  S.; Visanji,  N., P.; Mestre,  T., A.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Aldakheel,  A.; Connolly,  B., S.; Gasca-salas,  C.; Kern,  D., S.; Jain,  J.; Slow,  E., J.; Faust-Socher,  A.; Lang,  A., E.; Little,  M., A.;  and Marras,  C.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Parkinson's Disease</i>, 8(4): 503-510.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.3233/JPD-181389\"\n     onclick=\"log_download('arora-visanji-mestre-tsanas-aldakheel-connolly-gascasalas-kern-etal-investigatingvoiceasabiomarkerforleucinerichrepeatkinase2associatedparkinsonsdisease-2018', 'http://doi.org/10.3233/JPD-181389', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/arora-visanji-mestre-tsanas-aldakheel-connolly-gascasalas-kern-etal-investigatingvoiceasabiomarkerforleucinerichrepeatkinase2associatedparkinsonsdisease-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('arora-visanji-mestre-tsanas-aldakheel-connolly-gascasalas-kern-etal-investigatingvoiceasabiomarkerforleucinerichrepeatkinase2associatedparkinsonsdisease-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Investigating Voice as a Biomarker for leucine-rich repeat kinase 2-Associated Parkinson’s Disease},\n type = {article},\n year = {2018},\n pages = {503-510},\n volume = {8},\n id = {3f57ab1c-f2ea-3e53-beb6-196a618d25fe},\n created = {2019-02-22T10:49:29.986Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2019-02-22T11:11:42.667Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations (&#x27;aaah&#x27; sounds) were recorded from 7 individuals with LRRK2-associated Parkinson&#x27;s disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts.},\n bibtype = {article},\n author = {Arora, Siddharth and Visanji, Naomi P and Mestre, Tiago A and Tsanas, Athanasios and Aldakheel, Amaal and Connolly, Barbara S and Gasca-salas, Carmen and Kern, Drew S and Jain, Jennifer and Slow, Elizabeth J and Faust-Socher, Achinoam and Lang, Anthony E and Little, Max A and Marras, Connie},\n doi = {10.3233/JPD-181389},\n journal = {Journal of Parkinson&#x27;s Disease},\n number = {4}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations ('aaah' sounds) were recorded from 7 individuals with LRRK2-associated Parkinson's disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"shah-anand-strachan-ferry-lee-chapman-sandeman-stables-etal-highsensitivitytroponinintheevaluationofpatientswithsuspectedacutecoronarysyndromeasteppedwedgeclusterrandomisedcontrolledtrial-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  High-sensitivity troponin in the evaluation of patients with suspected acute coronary syndrome: a stepped-wedge, cluster-randomised controlled trial.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Shah,  A., S.; Anand,  A.; Strachan,  F., E.; Ferry,  A., V.; Lee,  K., K.; Chapman,  A., R.; Sandeman,  D.; Stables,  C., L.; Adamson,  P., D.; Andrews,  J., P., M.; Anwar,  M., S.; Hung,  J.; Moss,  A., J.; O'Brien,  R.; Berry,  C.; Findlay,  I.; Walker,  S.; Cruickshank,  A.; Reid,  A.; Gray,  A.; Collinson,  P., O.; Apple,  F., S.; McAllister,  D., A.; Maguire,  D.; Fox,  K., A., A.; Newby,  D., E.; Tuck,  C.; Harkess,  R.; Parker,  R., A.; Keerie,  C.; Weir,  C., J.; Mills,  N., L.; Investigators,  o., b., o., t., H.; Mills,  N., L.; Strachan,  F., E.; Tuck,  C.; Shah,  A., S., V.; Anand,  A.; Ferry,  A., V.; Lee,  K., K.; Chapman,  A., R.; Sandeman,  D.; Adamson,  P., D.; Stables,  C., L.; Marshall,  L.; Stewart,  S., D.; Fujisawa,  T.; Vallejos,  C., A.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Hautvast,  M.; McPherson,  J.; McKinlay,  L.; Newby,  D., E.; Fox,  K., A., A.; Berry,  C.; Walker,  S.; Weir,  C., J.; Ford,  I.; Gray,  A.; Collinson,  P., O.; Apple,  F., S.; Reid,  A.; Cruikshank,  A.; Findlay,  I.; Amoils,  S.; McAllister,  D., A.; Maguire,  D.; Stevens,  J.; Norrie,  J.; Andrews,  J., P., M.; Adamson,  P., D.; Moss,  A.; Anwar,  M., S.; Hung,  J.; Malo,  J.; Fischbacher,  C., M.; Croal,  B., L.; Leslie,  S., J.; Keerie,  C.; Parker,  R., A.; Walker,  A.; Harkess,  R.; Wackett,  T.; Armstrong,  R.; Flood,  M.; Stirling,  L.; MacDonald,  C.; Sadat,  I.; Finlay,  F.; Charles,  H.; Linksted,  P.; Young,  S.; Alexander,  B.;  and Duncan,  C.\n</span>\n\n  \n\n</span>\n\n  <i>The Lancet</i>, 392(10151): 919-928.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/s0140-6736(18)31923-8\"\n     onclick=\"log_download('shah-anand-strachan-ferry-lee-chapman-sandeman-stables-etal-highsensitivitytroponinintheevaluationofpatientswithsuspectedacutecoronarysyndromeasteppedwedgeclusterrandomisedcontrolledtrial-2018', 'http://doi.org/10.1016/s0140-6736(18)31923-8', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shah-anand-strachan-ferry-lee-chapman-sandeman-stables-etal-highsensitivitytroponinintheevaluationofpatientswithsuspectedacutecoronarysyndromeasteppedwedgeclusterrandomisedcontrolledtrial-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shah-anand-strachan-ferry-lee-chapman-sandeman-stables-etal-highsensitivitytroponinintheevaluationofpatientswithsuspectedacutecoronarysyndromeasteppedwedgeclusterrandomisedcontrolledtrial-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {High-sensitivity troponin in the evaluation of patients with suspected acute coronary syndrome: a stepped-wedge, cluster-randomised controlled trial},\n type = {article},\n year = {2018},\n pages = {919-928},\n volume = {392},\n id = {09e81b82-c9d8-3b5f-a82b-2e271684e710},\n created = {2019-02-22T10:49:30.089Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2019-02-22T10:49:30.089Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Shah, Anoop S.V. and Anand, Atul and Strachan, Fiona E. and Ferry, Amy V. and Lee, Kuan K. and Chapman, Andrew R. and Sandeman, Dennis and Stables, Catherine L and Adamson, Philip D and Andrews, Jack P M and Anwar, Mohamed S and Hung, John and Moss, Alistair J and O&#x27;Brien, Rachel and Berry, Colin and Findlay, Iain and Walker, Simon and Cruickshank, Anne and Reid, Alan and Gray, Alasdair and Collinson, Paul O and Apple, Fred S and McAllister, David A and Maguire, Donogh and Fox, Keith A A and Newby, David E and Tuck, Christopher and Harkess, Ronald and Parker, Richard A and Keerie, Catriona and Weir, Christopher J and Mills, Nicholas L and Investigators, on behalf of the HIGH-STEACS and Mills, Nicholas L and Strachan, Fiona E and Tuck, Christopher and Shah, Anoop S V and Anand, Atul and Ferry, Amy V and Lee, Kuan Ken and Chapman, Andrew R and Sandeman, Dennis and Adamson, Philip D and Stables, Catherine L and Marshall, Lucy and Stewart, Stacey D and Fujisawa, Takeshi and Vallejos, Catalina A and Tsanas, Athanasios and Hautvast, Mischa and McPherson, Jean and McKinlay, Lynn and Newby, David E and Fox, Keith A A and Berry, Colin and Walker, Simon and Weir, Christopher J and Ford, Ian and Gray, Alasdair and Collinson, Paul O and Apple, Fred S and Reid, Alan and Cruikshank, Anne and Findlay, Iain and Amoils, Shannon and McAllister, David A and Maguire, Donogh and Stevens, Jennifer and Norrie, John and Andrews, Jack P M and Adamson, Philip D and Moss, Alastair and Anwar, Mohamed S and Hung, John and Malo, Jonathan and Fischbacher, Colin M and Croal, Bernard L and Leslie, Stephen J and Keerie, Catriona and Parker, Richard A and Walker, Allan and Harkess, Ronnie and Wackett, Tony and Armstrong, Roma and Flood, Marion and Stirling, Laura and MacDonald, Claire and Sadat, Imran and Finlay, Frank and Charles, Heather and Linksted, Pamela and Young, Stephen and Alexander, Bill and Duncan, Chris},\n doi = {10.1016/s0140-6736(18)31923-8},\n journal = {The Lancet},\n number = {10151}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"tsanas-saunders-bilderbeck-palmius-goodwin-devos-clinicalinsightintolatentvariablesofpsychiatricquestionnairesformoodsymptomselfassessment-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://mental.jmir.org/2017/2/e15/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'tsanas-saunders-bilderbeck-palmius-goodwin-devos-clinicalinsightintolatentvariablesofpsychiatricquestionnairesformoodsymptomselfassessment-2017', 'http://mental.jmir.org/2017/2/e15/', event);\">\n    Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Saunders,  K.; Bilderbeck,  A.; Palmius,  N.; Goodwin,  G.;  and De Vos,  M.\n</span>\n\n  \n\n</span>\n\n  <i>JMIR Mental Health</i>, 4(2): e15.  2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://mental.jmir.org/2017/2/e15/\"\n     onclick=\"log_download('tsanas-saunders-bilderbeck-palmius-goodwin-devos-clinicalinsightintolatentvariablesofpsychiatricquestionnairesformoodsymptomselfassessment-2017', 'http://mental.jmir.org/2017/2/e15/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2196/mental.6917\"\n     onclick=\"log_download('tsanas-saunders-bilderbeck-palmius-goodwin-devos-clinicalinsightintolatentvariablesofpsychiatricquestionnairesformoodsymptomselfassessment-2017', 'http://doi.org/10.2196/mental.6917', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsanas-saunders-bilderbeck-palmius-goodwin-devos-clinicalinsightintolatentvariablesofpsychiatricquestionnairesformoodsymptomselfassessment-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsanas-saunders-bilderbeck-palmius-goodwin-devos-clinicalinsightintolatentvariablesofpsychiatricquestionnairesformoodsymptomselfassessment-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment},\n type = {article},\n year = {2017},\n keywords = {bipolar disorder,borderline personality disorder,depression,latent variable structure,mania,mhealth,mobile app,mood monitoring,outcome measures,patient reported},\n pages = {e15},\n volume = {4},\n websites = {http://mental.jmir.org/2017/2/e15/},\n id = {115fd00c-4e7c-3bcb-876d-f5c6a8f5daef},\n created = {2019-02-22T10:25:09.733Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2019-02-22T10:25:09.733Z},\n read = {true},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {BACKGROUND We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. OBJECTIVE The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. METHODS We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. RESULTS We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. CONCLUSIONS This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice.},\n bibtype = {article},\n author = {Tsanas, Athanasios and Saunders, Kate and Bilderbeck, Amy and Palmius, Niclas and Goodwin, Guy and De Vos, Maarten},\n doi = {10.2196/mental.6917},\n journal = {JMIR Mental Health},\n number = {2}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  BACKGROUND We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. OBJECTIVE The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. METHODS We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. RESULTS We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. CONCLUSIONS This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"palmius-tsanas-saunders-bilderbeck-geddes-goodwin-devos-detectingbipolardepressionfromgeographiclocationdata-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ieeexplore.ieee.org/document/7676335/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'palmius-tsanas-saunders-bilderbeck-geddes-goodwin-devos-detectingbipolardepressionfromgeographiclocationdata-2017', 'http://ieeexplore.ieee.org/document/7676335/', event);\">\n    Detecting Bipolar Depression from Geographic Location Data.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Palmius,  N.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; Saunders,  K., E., A.; Bilderbeck,  A., C.; Geddes,  J., R.; Goodwin,  G., M.;  and De Vos,  M.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Transactions on Biomedical Engineering</i>, 64(8): 1761-1771.  2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ieeexplore.ieee.org/document/7676335/\"\n     onclick=\"log_download('palmius-tsanas-saunders-bilderbeck-geddes-goodwin-devos-detectingbipolardepressionfromgeographiclocationdata-2017', 'http://ieeexplore.ieee.org/document/7676335/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Detecting Bipolar Depression from Geographic Location Data [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/TBME.2016.2611862\"\n     onclick=\"log_download('palmius-tsanas-saunders-bilderbeck-geddes-goodwin-devos-detectingbipolardepressionfromgeographiclocationdata-2017', 'http://doi.org/10.1109/TBME.2016.2611862', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/palmius-tsanas-saunders-bilderbeck-geddes-goodwin-devos-detectingbipolardepressionfromgeographiclocationdata-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('palmius-tsanas-saunders-bilderbeck-geddes-goodwin-devos-detectingbipolardepressionfromgeographiclocationdata-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Detecting Bipolar Depression from Geographic Location Data},\n type = {article},\n year = {2017},\n pages = {1761-1771},\n volume = {64},\n websites = {http://ieeexplore.ieee.org/document/7676335/},\n id = {ffc7e80b-6f1d-3422-b1e0-56f67193e1d9},\n created = {2019-02-22T10:25:09.737Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2019-02-22T10:25:09.737Z},\n read = {true},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Palmius, Niclas and Tsanas, Athanasios and Saunders, Kate E. A. and Bilderbeck, Amy C. and Geddes, John R. and Goodwin, Guy M. and De Vos, Maarten},\n doi = {10.1109/TBME.2016.2611862},\n journal = {IEEE Transactions on Biomedical Engineering},\n number = {8}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sansegundo-tsanas-gmezvilda-euclideandistancesasmeasuresofspeakersimilarityincludingidenticaltwinpairsaforensicinvestigationusingsourceandfiltervoicecharacteristics-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Euclidean Distances as measures of speaker similarity including identical twin pairs: A forensic investigation using source and filter voice characteristics.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  San Segundo,  E.; <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>;  and Gómez-Vilda,  P.\n</span>\n\n  \n\n</span>\n\n  <i>Forensic Science International</i>, 270: 25-38.  2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.forsciint.2016.11.020\"\n     onclick=\"log_download('sansegundo-tsanas-gmezvilda-euclideandistancesasmeasuresofspeakersimilarityincludingidenticaltwinpairsaforensicinvestigationusingsourceandfiltervoicecharacteristics-2017', 'http://doi.org/10.1016/j.forsciint.2016.11.020', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sansegundo-tsanas-gmezvilda-euclideandistancesasmeasuresofspeakersimilarityincludingidenticaltwinpairsaforensicinvestigationusingsourceandfiltervoicecharacteristics-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sansegundo-tsanas-gmezvilda-euclideandistancesasmeasuresofspeakersimilarityincludingidenticaltwinpairsaforensicinvestigationusingsourceandfiltervoicecharacteristics-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Euclidean Distances as measures of speaker similarity including identical twin pairs: A forensic investigation using source and filter voice characteristics},\n type = {article},\n year = {2017},\n keywords = {Acoustic analysis,Forensic phonetics,Pause fillers,Perceptual assessment,Twins,Voice quality},\n pages = {25-38},\n volume = {270},\n id = {51c365ff-57d8-3763-8992-b56a9cc2ecd1},\n created = {2019-02-22T11:03:04.627Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2019-02-22T11:03:04.627Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {There is a growing consensus that hybrid approaches are necessary for successful speaker characterization in Forensic Speaker Comparison (FSC); hence this study explores the forensic potential of voice features combining source and filter characteristics. The former relate to the action of the vocal folds while the latter reflect the geometry of the speaker&#x27;s vocal tract. This set of features have been extracted from pause fillers, which are long enough for robust feature estimation while spontaneous enough to be extracted from voice samples in real forensic casework. Speaker similarity was measured using standardized Euclidean Distances (ED) between pairs of speakers: 54 different-speaker (DS) comparisons, 54 same-speaker (SS) comparisons and 12 comparisons between monozygotic twins (MZ). Results revealed that the differences between DS and SS comparisons were significant in both high quality and telephone-filtered recordings, with no false rejections and limited false acceptances; this finding suggests that this set of voice features is highly speaker-dependent and therefore forensically useful. Mean ED for MZ pairs lies between the average ED for SS comparisons and DS comparisons, as expected according to the literature on twin voices. Specific cases of MZ speakers with very high ED (i.e. strong dissimilarity) are discussed in the context of sociophonetic and twin studies. A preliminary simplification of the Vocal Profile Analysis (VPA) Scheme is proposed, which enables the quantification of voice quality features in the perceptual assessment of speaker similarity, and allows for the calculation of perceptual–acoustic correlations. The adequacy of z-score normalization for this study is also discussed, as well as the relevance of heat maps for detecting the so-called phantoms in recent approaches to the biometric menagerie.},\n bibtype = {article},\n author = {San Segundo, Eugenia and Tsanas, Athanasios and Gómez-Vilda, Pedro},\n doi = {10.1016/j.forsciint.2016.11.020},\n journal = {Forensic Science International}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  There is a growing consensus that hybrid approaches are necessary for successful speaker characterization in Forensic Speaker Comparison (FSC); hence this study explores the forensic potential of voice features combining source and filter characteristics. The former relate to the action of the vocal folds while the latter reflect the geometry of the speaker's vocal tract. This set of features have been extracted from pause fillers, which are long enough for robust feature estimation while spontaneous enough to be extracted from voice samples in real forensic casework. Speaker similarity was measured using standardized Euclidean Distances (ED) between pairs of speakers: 54 different-speaker (DS) comparisons, 54 same-speaker (SS) comparisons and 12 comparisons between monozygotic twins (MZ). Results revealed that the differences between DS and SS comparisons were significant in both high quality and telephone-filtered recordings, with no false rejections and limited false acceptances; this finding suggests that this set of voice features is highly speaker-dependent and therefore forensically useful. Mean ED for MZ pairs lies between the average ED for SS comparisons and DS comparisons, as expected according to the literature on twin voices. Specific cases of MZ speakers with very high ED (i.e. strong dissimilarity) are discussed in the context of sociophonetic and twin studies. A preliminary simplification of the Vocal Profile Analysis (VPA) Scheme is proposed, which enables the quantification of voice quality features in the perceptual assessment of speaker similarity, and allows for the calculation of perceptual–acoustic correlations. The adequacy of z-score normalization for this study is also discussed, as well as the relevance of heat maps for detecting the so-called phantoms in recent approaches to the biometric menagerie.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tsanas-sansegundo-gmezvilda-exploringpausefillersinconversationalspeechforforensicphoneticsfindingsinaspanishcohortincludingtwins-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Exploring Pause Fillers in Conversational Speech for Forensic Phonetics: Findings in a Spanish Cohort Including Twins.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www-darth-group-com.filesusr.com/html/bebb15_3ebaa10c912c1baa868d24d66a9fab67.html\">Tsanas,  A.</a>; San Segundo,  E.;  and Gómez-Vilda,  P.\n</span>\n\n  \n\n</span>\n\n  In <i>8th International Conference of Pattern Recognition Systems</i>,  2017. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1049/cp.2017.0161\"\n     onclick=\"log_download('tsanas-sansegundo-gmezvilda-exploringpausefillersinconversationalspeechforforensicphoneticsfindingsinaspanishcohortincludingtwins-2017', 'http://doi.org/10.1049/cp.2017.0161', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tsanas-sansegundo-gmezvilda-exploringpausefillersinconversationalspeechforforensicphoneticsfindingsinaspanishcohortincludingtwins-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tsanas-sansegundo-gmezvilda-exploringpausefillersinconversationalspeechforforensicphoneticsfindingsinaspanishcohortincludingtwins-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Exploring Pause Fillers in Conversational Speech for Forensic Phonetics: Findings in a Spanish Cohort Including Twins},\n type = {inproceedings},\n year = {2017},\n keywords = {contour,forensic phonetics,fundamental frequency,pause fillers,speech signal processing},\n issue = {July},\n id = {65e7e85a-6877-30ef-991d-018a9bef9339},\n created = {2019-02-22T11:03:04.640Z},\n file_attached = {false},\n profile_id = {63c14ecf-8d9b-3014-983a-e77627cb99e3},\n group_id = {5c252342-c3f4-3788-aa2e-3a2bba078fe3},\n last_modified = {2019-02-22T11:03:04.640Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Tsanas, A. and San Segundo, E. and Gómez-Vilda, P.},\n doi = {10.1049/cp.2017.0161},\n booktitle = {8th International Conference of Pattern Recognition Systems}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);