Back to Search
Start Over
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.
- 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