Citation

Obeid JS, Tsalatsanis A, Chaphalkar C, Robinson S, Klein S, Cool S, Szwast E, Bunnell BE. Stud. Health Technol. Inform. 2024; 310: 1486-1487.

Copyright

(Copyright © 2024, IOS Press)

DOI

10.3233/SHTI231257

PMID

38269709

Abstract

Suicide risk models are critical for prioritizing patients for intervention. We demonstrate a reproducible approach for training text classifiers to identify patients at risk. The models were effective in phenotyping suicidal behavior (F1=.94) and moderately effective in predicting future events (F1=.63).

Language: en

Keywords

Humans; machine learning; *Suicidal Ideation; Suicidal behavior; reproducibility; text classification