Research By Drew Barzman, MD
Post Date: June 30, 2019 | Publish Date: May 1, 2018
A pilot study indicates that artificial intelligence may be useful in predicting which students are at higher risk of perpetrating school violence.
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 this risk. The study is published online May 1, 2018, in Psychiatric Quarterly.
“Previous violent behavior, impulsivity, school problems and negative attitudes were correlated with risk to others,” says lead author Drew Barzman, MD, Director of the Child and Adolescent Forensic Psychiatry Service.
The team evaluated 103 at-risk teens in 74 traditional schools throughout the U.S. Their machine learning algorithm achieved 91.02 percent accuracy, considered excellent, when using interview content to predict risks. The next step: gathering outcome data to assess whether machine learning could actually help prevent school violence.
|Original Title:||Automated Risk Assessment for School Violence: a Pilot Study|
|Published in:||Psychiatric Quarterly|
|Publish date:||May 1, 2018|
The Research Horizons blog features news and insights about the latest discoveries and innovations developed by the scientists of Cincinnati Children's. This blog does not provide medical advice, diagnosis, or treatment.