Automated detection and prediction of suicidal behavior from clinical notes using deep learning.

ABSTRACT:

Background

Deep learning approaches have tremendous potential to improve the predictive power of traditional suicide prediction models to detect and predict intentional self-harm (ISH). Existing research is limited by a general lack of consistent performance and replicability across sites. We aimed to validate a deep learning approach used in previous research to detect and predict ISH using clinical note text and evaluate its generalizability to other academic medical centers.

Methods

We extracted clinical notes from electronic health records (EHRs) of 1,538 patients with International Classification of Diseases codes for ISH and 3,012 matched controls without ISH codes. We evaluated the performance of two traditional bag-of-words models (i.e., Naïve Bayes, Random Forest) a

SUBMITTER: Bunnell BE 

PROVIDER: S-EPMC12435685 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature