Using administrative data to identify U.S. Army soldiers at high-risk of perpetrating minor violent crimes.

ABSTRACT: Growing concerns exist about violent crimes perpetrated by U.S. military personnel. Although interventions exist to reduce violent crimes in high-risk populations, optimal implementation requires evidence-based targeting. The goal of the current study was to use machine learning methods (stepwise and penalized regression; random forests) to develop models to predict minor violent crime perpetration among U.S. Army soldiers. Predictors were abstracted from administrative data available for all 975,057 soldiers in the U.S. Army 2004-2009, among whom 25,966 men and 2728 women committed a first founded minor violent crime (simple assault, blackmail-extortion-intimidation, rioting, harassment). Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, m

SUBMITTER: Rosellini AJ 

PROVIDER: S-EPMC5125854 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature