@article{dhaenens_clinmine:_nodate,
	title = {{ClinMine}: {Optimizing} the {Management} of {Patients} in {Hospital}},
	issn = {1959-0318},
	shorttitle = {{ClinMine}},
	url = {https://www.sciencedirect.com/science/article/pii/S1959031817301914},
	doi = {10.1016/j.irbm.2017.12.002},
	abstract = {Context
A better understanding of “patient pathway” thanks to data analysis can lead to better treatments for patients. The ClinMine project, supported by the French National Research Agency (ANR), aims at proposing, from various case studies, algorithmic and statistical models able to handle this type of pathway data, focusing primarily on hospital data.
Methods
This article presents two of these case studies, focusing on the integration of temporal data within analysis. First, the hypothesis that some aspects of the patient pathway can be described, even predicted, from the management process of the hospital medical mail is studied. Therefore a specific functional data analysis is driven, and several types of patients have been detected. The second case study deals with the detection of profiles through a biclustering of the patients. The difficulty to simultaneously deal with heterogeneous data, including temporal data is exposed and a method is proposed.
Results
Experiments are driven on real data coming from a hospital. Results on these data show the effectiveness of the two proposed methods.
Conclusion
The project ClinMine aimed at dealing with hospital data in order to provide a better understanding of “patient pathway”. The two methods proposed here show their ability to simultaneously deal with heterogeneous data, including temporal aspects, and manages to give information for the understanding of “patient pathway” (identification of interesting clusters of patients).},
	journal = {IRBM},
	author = {Dhaenens, C. and Jacques, J. and Vandewalle, V. and Vandromme, M. and Chazard, E. and Preda, C. and Amarioarei, A. and Chaiwuttisak, P. and Cozma, C. and Ficheur, G. and Kessaci, M. -E. and Perichon, R. and Taillard, J. and Bordet, R. and Lansiaux, A. and Jourdan, L. and Delerue, D. and Hansske, A.},
	keywords = {Electronic Health Records, Heterogeneous data, Hospital information system, Optimization algorithms, Patient pathway, Temporal data, statistical analysis}
}

{"_id":"7pSsfRppmc3AFquZj","bibbaseid":"dhaenens-jacques-vandewalle-vandromme-chazard-preda-amarioarei-chaiwuttisak-etal-clinmineoptimizingthemanagementofpatientsinhospital","downloads":0,"creationDate":"2018-01-25T09:53:09.564Z","title":"ClinMine: Optimizing the Management of Patients in Hospital","author_short":["Dhaenens, C.","Jacques, J.","Vandewalle, V.","Vandromme, M.","Chazard, E.","Preda, C.","Amarioarei, A.","Chaiwuttisak, P.","Cozma, C.","Ficheur, G.","Kessaci, M. -.","Perichon, R.","Taillard, J.","Bordet, R.","Lansiaux, A.","Jourdan, L.","Delerue, D.","Hansske, A."],"year":null,"bibtype":"article","biburl":"https://api.zotero.org/users/1597782/collections/WZKHFQJJ/items?key=7TMlJwUpZuksQylazX9lY1pz&format=bibtex&limit=100","bibdata":{"bibtype":"article","type":"article","title":"ClinMine: Optimizing the Management of Patients in Hospital","issn":"1959-0318","shorttitle":"ClinMine","url":"https://www.sciencedirect.com/science/article/pii/S1959031817301914","doi":"10.1016/j.irbm.2017.12.002","abstract":"Context A better understanding of “patient pathway” thanks to data analysis can lead to better treatments for patients. The ClinMine project, supported by the French National Research Agency (ANR), aims at proposing, from various case studies, algorithmic and statistical models able to handle this type of pathway data, focusing primarily on hospital data. Methods This article presents two of these case studies, focusing on the integration of temporal data within analysis. First, the hypothesis that some aspects of the patient pathway can be described, even predicted, from the management process of the hospital medical mail is studied. Therefore a specific functional data analysis is driven, and several types of patients have been detected. The second case study deals with the detection of profiles through a biclustering of the patients. The difficulty to simultaneously deal with heterogeneous data, including temporal data is exposed and a method is proposed. Results Experiments are driven on real data coming from a hospital. Results on these data show the effectiveness of the two proposed methods. Conclusion The project ClinMine aimed at dealing with hospital data in order to provide a better understanding of “patient pathway”. The two methods proposed here show their ability to simultaneously deal with heterogeneous data, including temporal aspects, and manages to give information for the understanding of “patient pathway” (identification of interesting clusters of patients).","journal":"IRBM","author":[{"propositions":[],"lastnames":["Dhaenens"],"firstnames":["C."],"suffixes":[]},{"propositions":[],"lastnames":["Jacques"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Vandewalle"],"firstnames":["V."],"suffixes":[]},{"propositions":[],"lastnames":["Vandromme"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Chazard"],"firstnames":["E."],"suffixes":[]},{"propositions":[],"lastnames":["Preda"],"firstnames":["C."],"suffixes":[]},{"propositions":[],"lastnames":["Amarioarei"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Chaiwuttisak"],"firstnames":["P."],"suffixes":[]},{"propositions":[],"lastnames":["Cozma"],"firstnames":["C."],"suffixes":[]},{"propositions":[],"lastnames":["Ficheur"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Kessaci"],"firstnames":["M.","-E."],"suffixes":[]},{"propositions":[],"lastnames":["Perichon"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Taillard"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Bordet"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Lansiaux"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Jourdan"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Delerue"],"firstnames":["D."],"suffixes":[]},{"propositions":[],"lastnames":["Hansske"],"firstnames":["A."],"suffixes":[]}],"keywords":"Electronic Health Records, Heterogeneous data, Hospital information system, Optimization algorithms, Patient pathway, Temporal data, statistical analysis","bibtex":"@article{dhaenens_clinmine:_nodate,\n\ttitle = {{ClinMine}: {Optimizing} the {Management} of {Patients} in {Hospital}},\n\tissn = {1959-0318},\n\tshorttitle = {{ClinMine}},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1959031817301914},\n\tdoi = {10.1016/j.irbm.2017.12.002},\n\tabstract = {Context\nA better understanding of “patient pathway” thanks to data analysis can lead to better treatments for patients. The ClinMine project, supported by the French National Research Agency (ANR), aims at proposing, from various case studies, algorithmic and statistical models able to handle this type of pathway data, focusing primarily on hospital data.\nMethods\nThis article presents two of these case studies, focusing on the integration of temporal data within analysis. First, the hypothesis that some aspects of the patient pathway can be described, even predicted, from the management process of the hospital medical mail is studied. Therefore a specific functional data analysis is driven, and several types of patients have been detected. The second case study deals with the detection of profiles through a biclustering of the patients. The difficulty to simultaneously deal with heterogeneous data, including temporal data is exposed and a method is proposed.\nResults\nExperiments are driven on real data coming from a hospital. Results on these data show the effectiveness of the two proposed methods.\nConclusion\nThe project ClinMine aimed at dealing with hospital data in order to provide a better understanding of “patient pathway”. The two methods proposed here show their ability to simultaneously deal with heterogeneous data, including temporal aspects, and manages to give information for the understanding of “patient pathway” (identification of interesting clusters of patients).},\n\tjournal = {IRBM},\n\tauthor = {Dhaenens, C. and Jacques, J. and Vandewalle, V. and Vandromme, M. and Chazard, E. and Preda, C. and Amarioarei, A. and Chaiwuttisak, P. and Cozma, C. and Ficheur, G. and Kessaci, M. -E. and Perichon, R. and Taillard, J. and Bordet, R. and Lansiaux, A. and Jourdan, L. and Delerue, D. and Hansske, A.},\n\tkeywords = {Electronic Health Records, Heterogeneous data, Hospital information system, Optimization algorithms, Patient pathway, Temporal data, statistical analysis}\n}\n\n","author_short":["Dhaenens, C.","Jacques, J.","Vandewalle, V.","Vandromme, M.","Chazard, E.","Preda, C.","Amarioarei, A.","Chaiwuttisak, P.","Cozma, C.","Ficheur, G.","Kessaci, M. -.","Perichon, R.","Taillard, J.","Bordet, R.","Lansiaux, A.","Jourdan, L.","Delerue, D.","Hansske, A."],"key":"dhaenens_clinmine:_nodate","id":"dhaenens_clinmine:_nodate","bibbaseid":"dhaenens-jacques-vandewalle-vandromme-chazard-preda-amarioarei-chaiwuttisak-etal-clinmineoptimizingthemanagementofpatientsinhospital","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S1959031817301914"},"keyword":["Electronic Health Records","Heterogeneous data","Hospital information system","Optimization algorithms","Patient pathway","Temporal data","statistical analysis"],"downloads":0},"search_terms":["clinmine","optimizing","management","patients","hospital","dhaenens","jacques","vandewalle","vandromme","chazard","preda","amarioarei","chaiwuttisak","cozma","ficheur","kessaci","perichon","taillard","bordet","lansiaux","jourdan","delerue","hansske"],"keywords":["electronic health records","heterogeneous data","hospital information system","optimization algorithms","patient pathway","temporal data","statistical analysis"],"authorIDs":[],"dataSources":["KyukCpdy7BkfWkbCe"]}