title: Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records.
abstract: 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.
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, Australia. karmakar@deakin.edu.au.
authors.forename: Chandan
authors.identifier: http://orcid.org/0000-0003-1814-0856
authors.initials: C
authors.lastname: Karmakar
authors.forename: Wei
authors.identifier: http://orcid.org/0000-0002-4711-7543
authors.initials: W
authors.lastname: Luo
authors.forename: Truyen
authors.identifier: http://orcid.org/0000-0001-6531-8907
authors.initials: T
authors.lastname: Tran
authors.forename: Michael
authors.identifier: http://orcid.org/0000-0002-5554-6946
authors.initials: M
authors.lastname: Berk
authors.forename: Svetha
authors.identifier: http://orcid.org/0000-0001-8675-6631
authors.initials: S
authors.lastname: Venkatesh
keywords: ICD-10 codes; electronic medical record; history of physical illnesses; suicide risk; suicide risk prediction model