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The Patient Path: a first approach

Authors :
Essifi, Rim
Inria Lille - Nord Europe
Institut National de Recherche en Informatique et en Automatique (Inria)
MOdel for Data Analysis and Learning (MODAL)
Laboratoire Paul Painlevé (LPP)
Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS)
Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille)
Équipe MODAL, Projet PATH
Essifi, Rim
Source :
CMStatistics 2022-The 15th International Conference of the ERCIM WG on Computational and Methodological Statistics, CMStatistics 2022-The 15th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2022, Londres, United Kingdom
Publication Year :
2022
Publisher :
HAL CCSD, 2022.

Abstract

International audience; European healthcare systems are faced with multiple challenges, including anaging population, an increase in chronic diseases and patients withmulti-morbidity, and limited financial and human resources. The response tothese challenges is based in particular on the organisation of care into carepathways. Namely, Once the data necessary for the construction of a carepathway are acquired and processed, one has to model the patient pathwaymathematically in a generic way. After that, using clustering algorithms, one canidentify patients subgroups, then, mine for common treatments, predict thefuture of patient pathways and answer clinicians’ questions. All these steps wouldlead to an automated processes which has to be evaluated by medical experts.Available statistical methods remain limited and inefficient to construct carepathways. Indeed, data obtained from health care providers and insurancecompanies are all time-dependent, highly heterogeneous, qualitative in part, withseveral thousand possible modalities and mainly made up of missing data. Wepropose an approach based on functional data analysis combined withlongitudinal data analysis in order to construct care pathways.

Details

Language :
English
Database :
OpenAIRE
Journal :
CMStatistics 2022-The 15th International Conference of the ERCIM WG on Computational and Methodological Statistics, CMStatistics 2022-The 15th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2022, Londres, United Kingdom
Accession number :
edsair.dedup.wf.001..fd1e42f24e5d6fb2191abe885a953be5