Finding warning markers: Leveraging natural language processing and machine learning technologies to detect risk of school violence.

Journal: International journal of medical informatics
Published Date:

Abstract

INTRODUCTION: School violence has a far-reaching effect, impacting the entire school population including staff, students and their families. Among youth attending the most violent schools, studies have reported higher dropout rates, poor school attendance, and poor scholastic achievement. It was noted that the largest crime-prevention results occurred when youth at elevated risk were given an individualized prevention program. However, much work is needed to establish an effective approach to identify at-risk subjects.

Authors

  • Yizhao Ni
    Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
  • Drew Barzman
    Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, USA. drew.barzman@cchmc.org.
  • Alycia Bachtel
    Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, USA.
  • Marcus Griffey
    Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, USA.
  • Alexander Osborn
    Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
  • Michael Sorter
    Division of Psychiatry, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.