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.
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
