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  2021 (5)
Performance of monosyllabic vs multisyllabic diadochokinetic exercises in evaluating Parkinson’s disease hypokinetic dysarthria from fuency distributions. Gómez-Vilda, P.; Gomez-Rodellar, A.; Palacios-Alonso, D.; and Tsanas, A. In 14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC), 2021.
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Remote assessment of Parkinson’s disease symptom severity using the simulated cellular mobile telephone network. Tsanas, A.; Little, M., A.; and Ramig, L., O. IEEE Access, 9: 11024-11036. 2021.
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Assessing Parkinson’s disease speech signal generalization of clustering results across three countries: findings in the Parkinson’s Voice Initiative study. Tsanas, A.; and Arora, S. In 14th International Joint Conference on Biomedical Systems and Technology (BIOSTEC), 2021.
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Acoustic to kinematic projection in Parkinson’s disease dysarthria. Gómez, A.; Tsanas, A.; Gómez, P.; Palacios, D.; Rodellar, V.; and Álvarez, A. Biomedical Signal Processing and Control,(in press). 2021.
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Smartphone speech testing for symptom assessment in rapid eye movement sleep behavior disorder and Parkinson’s disease. Arora, S.; Lo, C.; Hu, M.; and Tsanas, A. IEEE Access,(in press). 2021.
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  2020 (14)
Challenges of clustering multimodal clinical data: a review of applications in asthma subtyping. Horne, E.; Tibble, H.; Sheikh, A.; and Tsanas, A. JMIR Medical Informatics, 8(5): e16452. 2020.
Challenges of clustering multimodal clinical data: a review of applications in asthma subtyping [link]Website   doi   link   bibtex  
Assessing Preferred Proximity Between Different Types of Embryonic Stem Cells. Wang, M.; Tsanas, A.; Blin, G.; and Robertson, D. In 13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC), pages 377-381, 2020.
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Data-driven Insights Towards Risk Assessment of Postpartum Depression. Valavani, E.; Doudesis, D.; Kourtesis, I.; Chin, R., F.; MacIntyre, D., J.; Fletcher-Watson, S.; Boardman, J., P.; and Tsanas, A. In 13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC), pages 382-389, 2020.
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Large-scale Clustering of People Diagnosed with Parkinson’s Disease using Acoustic Analysis of Sustained Vowels: Findings in the Parkinson’s Voice Initiative Study. Tsanas, A.; and Arora, S. In 13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC), pages 369-376, 2020.
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Parkinson’s Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions. Álvarez, A.; Gómez, A.; Palacios, D.; Mekyska, J.; Tsanas, A.; Gómez, P.; and Martínez, R. In 13th International Joint Conference on Biomedical Systems and Technology (BIOSTEC), pages 359-368, 2020.
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Objective characterization of activity, sleep, and circadian rhythm patterns using a wrist-worn actigraphy sensor: insights into post-traumatic stress disorder. Tsanas, A.; Woodward, E.; and Ehlers, A. JMIR mHealth and uHealth, 8(4): e14306. 2020.
Objective characterization of activity, sleep, and circadian rhythm patterns using a wrist-worn actigraphy sensor: insights into post-traumatic stress disorder [link]Website   doi   link   bibtex  
Telemedicine Cognitive Behavioural Therapy for Anxiety after Stroke: Proof-of-Concept Randomized Controlled Trial. Chun, H., Y.; Carson, A., J.; Tsanas, A.; Dennis, M., S.; Mead, G., E.; and Whiteley, W., N. Stroke, 51: 2297-2306. 2020.
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Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications. 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.; and Ferrandez, J., M. Neurocomputing, 410: 237-270. 2020.
Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications [link]Website   link   bibtex  
Measuring and reporting treatment adherence: what can we learn by comparing two respiratory conditions?. Tibble, H.; Flook, M.; Sheikh, A.; Tsanas, A.; Horne, R.; Geest, S., D.; and Stagg, H., R. British Journal of Clinical Pharmacology, (in press). 2020.
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Eye-tracking for longitudinal assessment of social cognition in children born preterm. Dean, B.; Ginnell, L.; Ledsham, V.; Tsanas, A.; Telford, E.; Sparrow, S.; Fletcher-Watson, S.; and Boardman, J. Journal of Child Psychology and Psychiatry, (in press). 2020.
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Predicting pattern formation in embryonic stem cells using a minimalist, agent‑based probabilistic model. Wang, M.; Tsanas, A.; Blin, G.; and Robertson, D. Scientific Reports, 10: 16209. 2020.
Predicting pattern formation in embryonic stem cells using a minimalist, agent‑based probabilistic model [link]Website   doi   link   bibtex  
A data-driven typology of asthma medication adherence using cluster analysis. Tibble, H.; Chan, A.; Mitchell, E., A.; Horne, E.; Doudesis, D.; Horne, R.; Mizani, M., A.; Sheikh, A.; and Tsanas, A. Scientific Reports, 10: 14999. 2020.
A data-driven typology of asthma medication adherence using cluster analysis [link]Website   doi   link   bibtex   abstract  
Linkage of Primary Care Prescribing Records and Pharmacy Dispensing Records in Asthma Controller Medications. Tibble, H.; Lay-Flurrie, J.; Sheikh, A.; Horne, R.; Mizani, M.; and Tsanas, A. BMC Medical Research Methodology, 11: (in press). 2020.
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Beyond mobile apps: A survey of technologies for mental well-being. Woodward, K.; Kanjo, E.; Brown, D.; McGinnity, T.; Inkster, B.; Macintyre, D.; and Tsanas, A. IEEE Transactions on Affective Computing,(in press). 2020.
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  2019 (11)
Quantifying ultrasonic mouse vocalizations using acoustic analysis in a sueprvised statistical machine learning framework. Vogel, A.; Tsanas, A.; and Scattoni, M., L. Scientific Reports, 9(1): 8100. 2019.
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Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model. Tibble, H.; Tsanas, A.; Horne, E.; Horne, R.; Mizani, M., A.; Simpson, C., R.; and Sheikh, A. BMJ Open, 9(7): e028375. 2019.
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Machine Learning to Predict the Likelihood of Acute Myocardial Infarction. 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.; and MI3 collaborative Circulation, 140: 899-909. 2019.
Machine Learning to Predict the Likelihood of Acute Myocardial Infarction [link]Website   doi   link   bibtex   abstract  
Biomedical speech signal insights from a large scale cohort across seven countries: the Parkinson's voice initiative study. Tsanas, A.; and Arora, S. In 11th International Workshop Models and Analysis of Vocal Emissiong for Biomedical Applications (MAVEBA), pages 45-48, 2019.
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New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity. Tsanas, A. In 41st IEEE Engineering in Medicine and Biology Conference, pages 3412-3415, 2019.
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Assessing an application of spontaneous stressed speech - emotions portal. 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.; and Gómez-Vilda, P. 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.
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Exploring telephone-quality speech signals towards parkinson's disease assessment in a large acoustically non-controlled study. Tsanas, A.; and Arora, S. In 19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE), pages 953-956, 2019.
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Investigating motility and pattern formation in pluripotent stem cells through agent-based modeling. Wang, M.; Tsanas, A.; Blin, G.; and Robertson, D. In 19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE), pages 909-913, 2019. IEEE
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Heterogeneity in asthma medication adherence measurement. Tibble, H.; Chan, A.; Mitchell, E., A.; Horne, R.; Mizani, M., A.; Sheikh, A.; and Tsanas, A. In 19th International IEEE Conference on Bioinformatics and Bioengineering (IEEE BIBE), pages 899-903, 2019.
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Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice. Arora, S.; Baghai-Ravary, L.; and Tsanas, A. Journal of the Acoustical Society of America, 145(5): 2871-2884. 2019.
Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice [link]Website   doi   link   bibtex   abstract  
Applications of machine learning in real-life digital health interventions: Review of the literature. Triantafyllidis, A., K.; and Tsanas, A. Journal of Medical Internet Research, 21(4): 1-9. 2019.
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  2018 (4)
Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder. Carr, O.; Saunders, K., E., A.; Tsanas, A.; Palmius, N.; Geddes, J., R.; Foster, R.; Goodwin, G., M.; and De Vos, M. Scientific reports, 8: 1649. 2018.
Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder [link]Website   doi   link   bibtex  
Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder. Carr, O.; Saunders, K.; Bilderbeck, A.; Tsanas, A.; Palmius, N.; Geddes, J.; Foster, R.; De Vos, M.; and Goodwin, G. Translational Psychiatry, 8: 79. 2018.
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Investigating Voice as a Biomarker for leucine-rich repeat kinase 2-Associated Parkinson’s Disease. 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.; and Marras, C. Journal of Parkinson's Disease, 8(4): 503-510. 2018.
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High-sensitivity troponin in the evaluation of patients with suspected acute coronary syndrome: a stepped-wedge, cluster-randomised controlled trial. 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.; and Duncan, C. The Lancet, 392(10151): 919-928. 2018.
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  2017 (4)
Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment. Tsanas, A.; Saunders, K.; Bilderbeck, A.; Palmius, N.; Goodwin, G.; and De Vos, M. JMIR Mental Health, 4(2): e15. 2017.
Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment [link]Website   doi   link   bibtex   abstract  
Detecting Bipolar Depression from Geographic Location Data. Palmius, N.; Tsanas, A.; Saunders, K., E., A.; Bilderbeck, A., C.; Geddes, J., R.; Goodwin, G., M.; and De Vos, M. IEEE Transactions on Biomedical Engineering, 64(8): 1761-1771. 2017.
Detecting Bipolar Depression from Geographic Location Data [link]Website   doi   link   bibtex  
Euclidean Distances as measures of speaker similarity including identical twin pairs: A forensic investigation using source and filter voice characteristics. San Segundo, E.; Tsanas, A.; and Gómez-Vilda, P. Forensic Science International, 270: 25-38. 2017.
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Exploring Pause Fillers in Conversational Speech for Forensic Phonetics: Findings in a Spanish Cohort Including Twins. Tsanas, A.; San Segundo, E.; and Gómez-Vilda, P. In 8th International Conference of Pattern Recognition Systems, 2017.
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