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arXiv · 2610.06355

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

Yaniv Yacoby·Weiwei Pan·Hope Neveux·Taylor C. McGuire·Franchesca Castro-Ramirez·Anushka R. Patel·Matthew K. Nock
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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.

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Yaniv Yacoby, Weiwei Pan, Hope Neveux, Taylor C. McGuire, Franchesca Castro-Ramirez, Anushka R. Patel, Matthew K. Nock. 2026-10-05. Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support. https://arxiv.org/abs/2610.06355

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