@article{ref1,
title="An approach for data mining of electronic health record data for suicide risk management: database analysis for clinical decision support",
journal="JMIR mental health",
year="2019",
author="Berrouiguet, Sofian and Billot, Romain and Larsen, Mark Erik and Lopez-Castroman, Jorge and Jaussent, Isabelle and Walter, Michel and Lenca, Philippe and Baca-Garcia, Enrique and Courtet, Philippe",
volume="6",
number="5",
pages="e9766-e9766",
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><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><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><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><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><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.<p /> <p>Language: en</p>",
language="en",
issn="2368-7959",
doi="10.2196/mental.9766",
url="http://dx.doi.org/10.2196/mental.9766"
}