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Accepted for/Published in: JMIR Mental Health

Date Submitted: Feb 10, 2018
Open Peer Review Period: Feb 10, 2018 - Jul 26, 2018
Date Accepted: Jul 26, 2018
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

Berrouiguet S, Billot R, Larsen ME, Lopez Castroman J, Jaussent I, Walter M, Lenca P, Baca-Garcia E, Courtet P

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

JMIR Ment Health 2019;6(5):e9766

DOI: 10.2196/mental.9766

PMID: 31066693

PMCID: 6707587

A Data Mining Approach of Electronic Health Record Data for Suicide Risk Management: Toward Clinical Decision Support Systems in Suicide Prevention

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

ABSTRACT

Background:

In an e-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) in suicide prevention. Our objective was to describe the data mining module of a web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. A database of 2802 suicide attempters has been analysed. Clustering methods were used to identify groups of similar patients while regression trees are applied to estimate the number of suicide attempts among these patients. Three groups of patients are identified by clustering methods. Relevant risk factors explaining the number of suicide attempts are highlighted by regression trees. Data mining techniques can help to identify different groups of patients at risk of suicide reattempt.

Objective:

Our objective 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:

A database of 2802 suicide attempters has been analysed. Clustering methods were used to identify groups of similar patients while regression trees are applied to estimate the number of suicide attempts among these patients.

Results:

Three groups of patients are identified by clustering methods. Relevant risk factors explaining the number of suicide attempts are highlighted by regression trees. Data mining techniques can help to identify different groups of patients at risk of suicide reattempt.

Conclusions:

These results can be combined with web-based and smartphone based data to improve dynamic decision making for clinicians.


 Citation

Please cite as:

Berrouiguet S, Billot R, Larsen ME, Lopez Castroman J, Jaussent I, Walter M, Lenca P, Baca-Garcia E, Courtet P

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

JMIR Ment Health 2019;6(5):e9766

DOI: 10.2196/mental.9766

PMID: 31066693

PMCID: 6707587

Per the author's request the PDF is not available.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.