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Predicting risk of suicide attempt using history of physical illnesses from electronic medical records

Karmakar, Chandan, Luo, Wei, Tran, Truyen, Berk, Michael and Venkatesh, Svetha 2016, Predicting risk of suicide attempt using history of physical illnesses from electronic medical records, JMIR mental health, vol. 3, no. 3, pp. 1-10, doi: 10.2196/mental.5475.

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Title Predicting risk of suicide attempt using history of physical illnesses from electronic medical records
Author(s) Karmakar, Chandan
Luo, Wei
Tran, Truyen
Berk, Michael
Venkatesh, Svetha
Journal name JMIR mental health
Volume number 3
Issue number 3
Start page 1
End page 10
Total pages 10
Publisher JMIR Publications
Place of publication Toronto, Ont.
Publication date 2016-07-11
ISSN 2368-7959
Keyword(s) suicide risk
electronic medical record
history of physical illnesses
ICD-10 codes
suicide risk prediction model
Summary BACKGROUND: Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk.

OBJECTIVE: The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data.

METHODS: We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC).

RESULTS: The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians.

CONCLUSIONS: This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk.
Language eng
DOI 10.2196/mental.5475
Field of Research 080109 Pattern Recognition and Data Mining
110302 Clinical Chemistry (Diagnostics)
111716 Preventive Medicine
Socio Economic Objective 920410 Mental Health
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085314

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.