School violence has increased over the past ten years. This study evaluated students using a more standard and sensitive method to help identify students who are at high risk for school violence. Participants (ages 12–18) were active students in 74 traditional schools (i.e. Collateral information was gathered from guardians before participants were evaluated. School risk evaluations were performed with each participant, and audio recordings from the evaluations were later transcribed and manually annotated.

The researchers found that machine learning -- the science of getting computers to learn over time without human intervention -- is as accurate as a team of child and adolescent psychiatrists, including a forensic psychiatrist, in determining risk for school violence. "Previous violent behavior, impulsivity, school problems and negative attitudes were correlated with risk to others," says Drew Barzman, MD, a child forensic psychiatrist at Cincinnati Children's Hospital Medical Center and lead author of the study. "Our risk assessments were focused on predicting any type of physical aggression at school. We did not gather outcome data to assess whether machine learning could actually help prevent school violence. That is our next goal."