Words matter: Multimodal Suicide Risk Prediction from Veterans Health Administration Clinical Notes
Jiang Gui, Associate Professor, Biomedical Data Science, Dartmouth College
Abstract: In this talk, we demonstrate that integrating unstructured clinical narratives with structured electronic health record (EHR) data enhances suicide risk prediction for U.S. Veterans, outperforming models that rely on structured data alone. By analyzing a retrospective matched case-control cohort of 4,584 Veterans who died by suicide and 22,657 controls, we compared traditional count-based text features against pretrained contextual large language model (LLM) embeddings, such as Clinical Longformer and BioClinicalBERT. We found that while Adaptive Mixture Categorization (AMC) improves the utility of skewed linguistic data, contextual LLM embeddings consistently provide comparable or superior predictive power, particularly within low- and moderate-risk tiers where structured indicators may be less obvious. Our multimodal approach, which integrated 66 structured patient characteristics with text features, yielded substantial performance gains, increasing AUROC by approximately 0.07–0.11 across various risk tiers and time windows. Furthermore, our temporal analysis revealed that while long-term data (270 days) is most informative for low-risk patients, short-term windows (<30 days) are critical for high-risk individuals. Using SHAP-based interpretability and topic modeling, we identified clinically coherent themes that shift semantically as risk increases, providing a context-aware framework for improving suicide prevention efforts within the Veterans Health Administration.
Cost: free
Kisa Kowal
(847) 491-3974
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