Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support

2026-10-05

AI Summary

This study addresses the challenge that high patient heterogeneity and the low base rate of suicide events constrain the accuracy of personalized risk prediction. To overcome this, we propose a latent similarity Gaussian process that embeds patients into a continuous latent space to jointly model inter-patient similarity and individual risk trajectories. Specifically, an identifiable dual-channel similarity kernel is designed to selectively leverage peer information, while a correction mechanism is introduced to mitigate model degeneration induced by mean-field variational inference. Evaluated on dense longitudinal data, the proposed approach significantly outperforms population-level, individual-level, and hierarchical baselines. Notably, it substantially improves predictive precision for first-time suicide events, thereby establishing a novel paradigm for individualized risk assessment in clinical settings.

Abstract

Forecasting suicide risk is difficult due to the high heterogeneity of patients and the low base rate of suicide-related events (SREs). We present Latent Similarity Gaussian Processes (LSGPs), which embed patients in a continuous latent space to jointly model similarity and forecast risk. By selectively drawing information from latent peers, LSGPs better capture individualized risk trajectories, generalizing nomothetic (pooled), idiographic (per-patient), and hierarchical frameworks. Our contributions are: (1) an identifiable two-channel Similarity Kernel; (2) proof that the standard model-fitting algorithm, mean-field variational inference, collapses LSGPs to nomothetic models, along with a fix; and (3) empirical results on intensive longitudinal suicide data showing LSGPs outperform nomothetic, idiographic, and hierarchical models for next-week risk forecasting, with the largest gains in forecasting first-occurrence SREs.

Problem

• suicide risk forecasting
• patient heterogeneity
• low base rate
• personalized prediction
• clinical decision-support

Innovation

• Latent Similarity Gaussian Processes
• Similarity Kernel
• Mean-field Variational Inference
• Personalized Forecasting
• Suicide Risk