title: Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments.
abstract: BACKGROUND
  To date, our ability to accurately identify patients at high risk from suicidal behaviour, and thus to target interventions, has been fairly limited. This study examined a large pool of factors that are potentially associated with suicide risk from the comprehensive electronic medical record (EMR) and to derive a predictive model for 1-6 month risk.
  
  
  METHODS
  7,399 patients undergoing suicide risk assessment were followed up for 180 days. The dataset was divided into a derivation and validation cohorts of 4,911 and 2,488 respectively. Clinicians used an 18-point checklist of known risk factors to divide patients into low, medium, or high risk. Their predictive ability was compared with a risk stratification model derived from the EMR data. The model was based on the continuation-ratio ordinal regression method coupled with lasso (which stands for least absolute shrinkage and selection operator).
  
  
  RESULTS
  In the year prior to suicide assessment, 66.8% of patients attended the emergency department (ED) and 41.8% had at least one hospital admission. Administrative and demographic data, along with information on prior self-harm episodes, as well as mental and physical health diagnoses were predictive of high-risk suicidal behaviour. Clinicians using the 18-point checklist were relatively poor in predicting patients at high-risk in 3 months (AUC 0.58, 95% CIs: 0.50 - 0.66). The model derived EMR was superior (AUC 0.79, 95% CIs: 0.72 - 0.84). At specificity of 0.72 (95% CIs: 0.70-0.73) the EMR model had sensitivity of 0.70 (95% CIs: 0.56-0.83).
  
  
  CONCLUSIONS
  Predictive models applied to data from the EMR could improve risk stratification of patients presenting with potential suicidal behaviour. The predictive factors include known risks for suicide, but also other information relating to general health and health service utilisation.
authors.forename: Truyen
authors.initials: T
authors.lastname: Tran
authors.forename: Wei
authors.initials: W
authors.lastname: Luo
authors.forename: Dinh
authors.initials: D
authors.lastname: Phung
authors.forename: Richard
authors.initials: R
authors.lastname: Harvey
authors.forename: Michael
authors.initials: M
authors.lastname: Berk
authors.forename: Richard Lee
authors.initials: RL
authors.lastname: Kennedy
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong 3220, Australia. svetha.venkatesh@deakin.edu.au.
authors.forename: Svetha
authors.initials: S
authors.lastname: Venkatesh
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001315:Australia / Q000453:epidemiology; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D008487:Medical History Taking / Q000706:statistics & numerical data; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D012189:Retrospective Studies; D018570:Risk Assessment / Q000706:statistics & numerical data; D012307:Risk Factors; D059020:Suicidal Ideation; D013405:Suicide / Q000523:psychology / Q000706:statistics & numerical data*; D055815:Young Adult; D000092864:Suicide Prevention