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

Sofian Berrouiguet,Philippe Lenca,Jorge Lopez-Castroman,Mark Erik Larsen,Isabelle Jaussent,Michel Walter,Philippe Courtet,Enrique Baca-García,Romain Billot

doi:10.2196/mental.9766

Abstract

BackgroundIn 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.ObjectiveThe 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.MethodsWe 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.ResultsWe identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees.ConclusionsData 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.

Highlights

Suicide Risk AssessmentOver 800,000 people die of suicide every year, and it is estimated that for each suicide, there may have been >20 other attempted suicides
The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians
We present the main results of a data mining process on a sample of suicide attempters to first identify groups of similar patients and identify risk factors associated with the number of suicide attempts

Summary

Introduction

Suicide Risk AssessmentOver 800,000 people die of suicide every year, and it is estimated that for each suicide, there may have been >20 other attempted suicides. Clinical [3], environmental [4], and genetic [2] suicide risk factors have been intensively studied among suicide attempters. Attempters provide data to identify suicide-related risk factors, and such at-risk patients are a privileged target for proper prevention and intervention strategies (eg, by mitigating risk factors or by maintaining contact with clinical support) [5]. There is an urgent need for an innovative tool that could integrate both empirical and structured assessment to support decision making in suicide prevention. 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

Methods

Results

Discussion

Conclusion