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Continuous-Time and Dynamic Suicide Attempt Risk Prediction with Neural Ordinary Differential Equations
Yi-Han Sheu1,2,3,4, Jaak Simm5, Bo Wang1,2,3,4
1Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
Medrxiv : the Preprint Server for Health Sciences
|March 11, 2024
Summary
This study introduces novel AI models to predict dynamic suicide-related behavior risk. These models offer continuous risk assessment, improving suicide prevention strategies.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Public Health
Background:
- Suicide is a leading cause of death in the US, with increasing mortality.
- Current suicide-related behavior (SRB) risk assessments are static, creating gaps in monitoring fluctuating risk.
- Existing methods fail to capture the dynamic and continuous nature of SRB risk.
Conclusions:
- Novel AI models can effectively predict dynamic suicide-related behavior risk.
- These models represent a significant advancement over static risk assessment methods.
- The findings support the development of proactive suicide prevention strategies based on continuous risk monitoring.
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