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.

Method

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 variab

SUBMITTER: Rosellini AJ 

PROVIDER: S-EPMC5111361 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature