title: Predicting non-familial major physical violent crime perpetration in the US Army from administrative data.
abstract: BACKGROUND
  Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among US Army soldiers.
  
  
  METHODS
  A consolidated administrative database for all 975 057 soldiers in the US Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Of these soldiers, 5771 committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression, random forests, penalized regressions). The model was then validated in an independent 2011-2013 sample.
  
  
  RESULTS
  Key predictors were indicators of disadvantaged social/socioeconomic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver-operating characteristic curve was 0.80-0.82 in 2004-2009 and 0.77 in the 2011-2013 validation sample. Of all administratively recorded crimes, 36.2-33.1% (male-female) were committed by the 5% of soldiers having the highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample.
  
  
  CONCLUSIONS
  Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks.
authors.affiliation: Department of Health Care Policy,Harvard Medical School,Boston,MA,USA.
authors.forename: A J
authors.initials: AJ
authors.lastname: Rosellini
authors.affiliation: School of Law,University of Virginia,Charlottesville,VA,USA.
authors.forename: J
authors.initials: J
authors.lastname: Monahan
authors.affiliation: National Center for PTSD,VA Boston Healthcare System,Boston,MA,USA.
authors.forename: A E
authors.initials: AE
authors.lastname: Street
authors.affiliation: Institute for Social Research, University of Michigan,Ann Arbor,MI,USA.
authors.forename: S G
authors.initials: SG
authors.lastname: Heeringa
authors.affiliation: Department of Health Care Policy,Harvard Medical School,Boston,MA,USA.
authors.forename: E D
authors.initials: ED
authors.lastname: Hill
authors.affiliation: Department of Health Care Policy,Harvard Medical School,Boston,MA,USA.
authors.forename: M
authors.initials: M
authors.lastname: Petukhova
authors.affiliation: Predictive Medicine Group,Boston Children's Hospital and Harvard Medical School,Boston,MA,USA.
authors.forename: B Y
authors.initials: BY
authors.lastname: Reis
authors.affiliation: Department of Health Care Policy,Harvard Medical School,Boston,MA,USA.
authors.forename: N A
authors.initials: NA
authors.lastname: Sampson
authors.affiliation: Darla Moore School of Business,University of South Carolina,Columbia,South Carolina,USA.
authors.forename: P
authors.initials: P
authors.lastname: Bliese
authors.affiliation: Office of Science Policy, Planning and Communications,National Institute of Mental Health,Bethesda,MD,USA.
authors.forename: M
authors.initials: M
authors.lastname: Schoenbaum
authors.affiliation: Departments of Psychiatry and Family Medicine & Public Health,University of California San Diego,La Jolla,CA,USA.
authors.forename: M B
authors.initials: MB
authors.lastname: Stein
authors.affiliation: Department of Psychiatry,Center for the Study of Traumatic Stress,Uniformed Services University School of Medicine,Bethesda,MD,USA.
authors.forename: R J
authors.initials: RJ
authors.lastname: Ursano
authors.affiliation: Department of Health Care Policy,Harvard Medical School,Boston,MA,USA.
authors.forename: R C
authors.initials: RC
authors.lastname: Kessler
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D019540:Area Under Curve; D003415:Crime / Q000706:statistics & numerical data; D005260:Female; D005391:Firesetting Behavior / Q000453:epidemiology*; D006708:Homicide / Q000706:statistics & numerical data*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D001523:Mental Disorders / Q000453:epidemiology* / Q000628:therapy; D008875:Middle Aged; D008889:Military Personnel / Q000706:statistics & numerical data*; D016017:Odds Ratio; D012372:ROC Curve; D012044:Regression Analysis; D018570:Risk Assessment; D012923:Social Class; D014481:United States / Q000453:epidemiology; D014754:Violence / Q000706:statistics & numerical data*; D055815:Young Adult
keywords: Actuarial model; crime perpetration; machine learning; military violence; physical violence; risk model