Timing Is Everything: Modeling the Temporal Dynamics of Suicidal Ideation
Year: 2026
Type: preprint
Abstract: Objective: Suicidal ideation (SI) is a critical clinical concern, yet most predictive approaches rely on static or aggregate measures that overlook its session-to-session dynamics. This study aimed to improve next-session SI prediction by integrating dynamic item-level data and identifying the clinical features that contribute most strongly throughout treatment. Methods: Data from 2,045 outpatients were analyzed across the first 10 sessions. A transformer-based time-series model was trained to predict next-session SI using prior-session item-level scores and was compared against a static elastic-net baseline. SHAP values were calculated to quantify feature contributions at both group and individual levels. Results: The dynamic model significantly outperformed the static baseline (average R² = .95 vs. .12), and performance remained improved even without autoregressive inputs (average R² = .24). While prior-session SI was consistently the strongest predictor, depressive affect, hopelessness, meaning in life, and anxiety-related symptoms also provided significant predictive value. SHAP analyses further revealed temporal fluctuations and patient-specific patterns in influential features. Conclusion: Dynamic, interpretable models using item-level data substantially enhance short-term SI prediction. These findings provide clinical insights into which symptoms are most relevant for specific patients at different times, supporting more responsive and personalized suicide risk monitoring.
Source: PsyArXiv (OSF Preprints)
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7213516137