Exploring the Perspectives of Pediatric Health Care Providers, Youth Patients, and Caregivers on Machine Learning Suicide Risk Classification: Mixed Methods Study.

Journal: Journal of medical Internet research
Published Date: Aug 19, 2025

Abstract

BACKGROUND: Suicide was the second leading cause of death for youth aged between 10 and 24 years in 2023, necessitating improved risk identification to better identify those in need of support. While machine learning (ML) applied to electronic health records shows promise in improving risk identification, further research on the perspectives of these tools is needed to better inform implementation strategies.

Authors

  • Rohan R Dayal
    Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, United States, 1 9172837220.
  • Pua Lani Yang
    Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, United States, 1 9172837220.
  • Laura Nicole Sisson
    Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
  • Mira Bajaj
    Harvard Medical School, Mass General Brigham McLean, Boston, MA, United States.
  • Shannon Archuleta
    Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, United States, 1 9172837220.
  • Sophie Yao
    Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Daniel H Park
    Johns Hopkins University, Baltimore, MD, United States.
  • Hanae Fujii-Rios
    Department of Pediatrics, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.
  • Emily E Haroz
    Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.

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