AI Case Study

Cincinnati Children’s Hospital Medical Centre predicted at expert-level 91% accuracy which students are at higher risk of perpetrating school violence using machine learning on interview scores

A pilot study indicates that AI may be useful in predicting which students are at higher risk of perpetrating school violence. At 91% accuracy it is as accurate as a team of child and adolescent psychiatrists, including a forensic psychiatrist.

Industry

Healthcare

Healthcare Providers And Services

Project Overview

"This study evaluated students using a more standard and sensitive method to help identify students who are at high risk for school violence. 103 participants were recruited through Cincinnati Children’s Hospital Medical Center (CCHMC) from psychiatry outpatient clinics, the inpatient units, and the emergency department. Participants (ages 12–18) were active students in 74 traditional schools (i.e. non-online education). 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 BRACHA (School Version) and the School Safety Scale (SSS), both 14-item scales, were used. A template of open-ended questions was also used. This analysis included 103 participants who were recruited from 74 different schools.

Audio recordings from the evaluations were transcribed and manually annotated. The students, as it turned out, were relatively equally divided between moderate- to high-risk, and low-risk, according to two scales that the team developed and validated in previous research.

Of the 103 students evaluated, 55 were found to be moderate to high risk and 48 were found to be low risk based on the paper risk assessments including the BRACHA and SSS. Both the BRACHA and the SSS were highly correlated with risk of violence to others (Pearson correlations>0.82). There were significant differences in BRACHA and SSS total scores between low risk and high risk to others groups (p-values)."

Reported Results

The machine learning algorithm that the researchers developed achieved an accuracy rate of 91.02 percent, considered excellent, when using interview content to predict risk of school violence. The rate increased to 91.45 percent when demographic and socioeconomic data were added.

Technology

Function

Risk

Security

Background

According to Neuroscience news: "'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.'

'The machine learning algorithm, based only on the participant’s interview, was almost as accurate in assessing risk levels as a full assessment by our research team, including gathering information from parents and the school, a review of records when available, and scoring on the two scales we developed,' says Yizhao Ni, PhD, a computational scientist in the division of biomedical informatics at Cincinnati Children’s and co-author of the study."

Benefits

Data