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lundi 26 août 2019

ETUDE RECHERCHE Une approche pour l'exploration de données de dossiers de santé électroniques pour la gestion du risque de suicide: analyse de base de données pour l'aide à la décision clinique

An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support
Sofian Berrouiguet 1, 2 Romain Billot 1, 3 Mark Erik Larsen 4 Jorge Lopez-Castroman 5 Isabelle Jaussent 6, 7 Michel Walter 8 Philippe Lenca 1, 3 Enrique Baca-Garcia 9 Philippe Courtet 6, 7
1 Lab-STICC_IMTA_CID_DECIDE
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 CHU - Brest- Psychiatrie - CHRU Brest - Psychiatrie Adulte
3 IMT Atlantique - LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information
4 Black Dog Institute [Sydney, Australia]
5 Neuropsychiatrie : recherche épidémiologique et clinique
6 UM - Université de Montpellier
7 CHRU Montpellier - Centre Hospitalier Régional Universitaire [Montpellier]
8 CHRU Brest - Centre Hospitalier Régional Universitaire de Brest
9 Department of Psychiatry (Hospital Universitario Fundacion Jimenez Diaz )
Abstract : Background: In an electronic health context, combining traditional structured clinical assessment methods and routine electronic health-based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention. Objective: The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. Methods: We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. Results: We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. Conclusions: Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians.
berrouiguet_etal_JMIR-MH_2019....
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