Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling
arXiv:2508.17090v4 Announce Type: replace-cross Abstract: Ecological Momentary Assessment (EMA) studies enable the collection of high-frequency self-reports of suicidal thoughts and behaviors (STBs) via smartphones. Latent stochastic differential equations (SDEs) are a promising model class for EMA data, as it is irregularly sampled, noisy, and partially observed. But SDE-based models suffer from two key limitations. (a) These models often violate domain constraints, undermining scientific validity and clinical trust of the model. (b) Training is numerically unstable without ad hoc fixes (e.g.
The continuous development in AI and machine learning techniques, particularly in areas like stochastic differential equations, is enabling more sophisticated applications in sensitive domains like mental health. The paper addresses current limitations in SDE models for real-world, high-frequency health data.
Improved modeling of complex, real-time health data, specifically suicide risk, can lead to more effective early intervention strategies and personalized mental health support. This advance addresses critical issues of validity and stability in applying AI to human behavior.
This research introduces methods to overcome key limitations of SDE-based models, such as violating domain constraints and numerical instability, making them more reliable for clinical applications. This could enhance the trustworthiness and practical utility of AI in mental health assessment.
- · Mental health researchers and clinicians
- · Patients at risk of suicide
- · Wearable tech and smartphone health app developers
- · Machine learning researchers in health AI
- · Traditional, less dynamic risk assessment methods
- · AI models that lack robust methods for domain constraints and stability
- · Clinical practices relying solely on periodic, retrospective data
More accurate and stable AI models for predicting and assessing suicide risk become available for research and clinical trials.
Improved early warning systems and personalized interventions for mental health could significantly reduce suicide rates.
The success in suicide risk modeling could pave the way for similar robust AI applications in other sensitive and complex health domains.
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