ArticleNPJ digital medicine2025
Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis.PLoS medicine · 2025Pooled it
- Comparing patterns of recent mental health service use for predicting suicidal events following emergency department mental health visits in the United States: A national cohort study.Social psychiatry and psychiatric epidemiology · 2026Article
- From monitoring to intervention: a closed-loop digital health framework for gaming disorder.Frontiers in public health · 2026Review
- A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations.Frontiers in psychiatry · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
Current clinician-based and automated risk assessment methods treat the risk of suicide-related behaviors (SRBs) as static, while in actual clinical practice, SRB risk fluctuates over time. Here, we develop two closely related model classes, Event-GRU-ODE and Event-GRU-Discretized, that can predict the dynamic risk of events as a continuous trajectory across future time points, even without new observations, while updating these estimates as new data become available. Models were trained and validated for SRB prediction using a large electronic health record database. Both models demonstrated high discrimination (e.g., Event-GRU-ODE AUROC = 0.93, AUPRC = 0.10, relative risk = 13.4 at 95% specificity in a low-prevalence [0.15%] general cohort with a 1.5-year prediction window). This work provides an initial step toward developing novel suicide prevention strategies based on dynamic changes in risk.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.