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An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support.

An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support.

Authors :
Berrouiguet S
Billot R
Larsen ME
Lopez-Castroman J
Jaussent I
Walter M
Lenca P
Baca-García E
Courtet P
Source :
JMIR mental health [JMIR Ment Health] 2019 May 07; Vol. 6 (5), pp. e9766. Date of Electronic Publication: 2019 May 07.
Publication Year :
2019

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.<br />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.<br />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.<br />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.<br />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.<br /> (©Sofian Berrouiguet, Romain Billot, Mark Erik Larsen, Jorge Lopez-Castroman, Isabelle Jaussent, Michel Walter, Philippe Lenca, Enrique Baca-García, Philippe Courtet. Originally published in JMIR Mental Health (http://mental.jmir.org), 07.05.2019.)

Details

Language :
English
ISSN :
2368-7959
Volume :
6
Issue :
5
Database :
MEDLINE
Journal :
JMIR mental health
Publication Type :
Academic Journal
Accession number :
31066693
Full Text :
https://doi.org/10.2196/mental.9766