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Automated detection and prediction of suicidal behavior from clinical notes using deep learning
Brian E Bunnell1, Athanasios Tsalatsanis2, Chaitanya Chaphalkar2
1Department of Psychiatry and Behavioral Neurosciences, Morsani College of Medicine, University of South Florida, Tampa, Florida, United States of America.
Plos One
|September 15, 2025
Summary
Deep learning models accurately detect and predict intentional self-harm (ISH) using clinical notes. This approach is feasible and replicable across institutions, improving upon traditional methods for suicide prediction.
Area of Science:
- Artificial Intelligence
- Clinical Informatics
- Computational Psychiatry
Background:
- Deep learning (DL) models offer potential for enhanced suicide prediction.
- Existing research faces challenges in performance consistency and site-to-site replicability.
- Validation of DL for intentional self-harm (ISH) detection and prediction is needed across diverse academic medical centers.
Purpose of the Study:
- To validate a DL approach for detecting and predicting ISH using clinical notes.
- To evaluate the generalizability of DL models for ISH prediction across multiple institutions.
Main Methods:
- Extracted clinical notes from EHRs for 1,538 ISH patients and 3,012 controls.
- Evaluated traditional bag-of-words models (Naïve Bayes, Random Forest) and CNN models (CNNr, CNNw).
- Assessed model performance for detecting ISH within 24 hours and predicting ISH 1-6 months prior.
Main Results:
- CNN models significantly outperformed bag-of-words models in detecting concurrent ISH (AUCs .99, F1 0.94).
- CNN models showed superior performance over Naïve Bayes in predicting future ISH (AUCs 0.81-0.82, F1 0.61-0.64).
Conclusions:
- Leveraging EHRs and DL models for ISH detection/prediction using clinical notes is feasible and replicable.
- The study demonstrates the potential for multi-institutional validation of DL approaches for suicide risk assessment.
- Future research will explore broader multi-site validation using diverse EHR data.
