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

arXiv: https://arxiv.org/abs/2610.06355 · HTML: https://mlsanity.com/paper/2610.06355/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 83 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Builds on (most cited first, 3 of 3)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks](https://mlsanity.com/paper/2007.07124/) (2020)
- [Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding](https://mlsanity.com/paper/1902.03376/) (2019)

## Abstract (arXiv)

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.