Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01552-y |
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| author | Yi-han Sheu Jaak Simm Bo Wang Hyunjoon Lee Jordan W. Smoller |
| author_facet | Yi-han Sheu Jaak Simm Bo Wang Hyunjoon Lee Jordan W. Smoller |
| author_sort | Yi-han Sheu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8fc19ecff35c4ed29332199df8d86028 |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-8fc19ecff35c4ed29332199df8d860282025-08-20T02:56:17ZengNature Portfolionpj Digital Medicine2398-63522025-03-018111110.1038/s41746-025-01552-yContinuous time and dynamic suicide attempt risk prediction with neural ordinary differential equationsYi-han Sheu0Jaak Simm1Bo Wang2Hyunjoon Lee3Jordan W. Smoller4Center for Precision Psychiatry, Massachusetts General HospitalDepartment of Electrical Engineering, KU LeuvenCenter for Precision Psychiatry, Massachusetts General HospitalCenter for Precision Psychiatry, Massachusetts General HospitalCenter for Precision Psychiatry, Massachusetts General HospitalAbstract 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.https://doi.org/10.1038/s41746-025-01552-y |
| spellingShingle | Yi-han Sheu Jaak Simm Bo Wang Hyunjoon Lee Jordan W. Smoller Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations npj Digital Medicine |
| title | Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations |
| title_full | Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations |
| title_fullStr | Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations |
| title_full_unstemmed | Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations |
| title_short | Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations |
| title_sort | continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations |
| url | https://doi.org/10.1038/s41746-025-01552-y |
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