{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.06355",
  "author_total": 7,
  "authors": [
    {
      "name": "Yaniv Yacoby"
    },
    {
      "name": "Weiwei Pan"
    },
    {
      "name": "Hope Neveux"
    },
    {
      "name": "Taylor C. McGuire"
    },
    {
      "name": "Franchesca Castro-Ramirez"
    },
    {
      "name": "Anushka R. Patel"
    },
    {
      "name": "Matthew K. Nock"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/03wqmva0d0tjd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.06355",
  "fulltext_url": null,
  "id": "03wqmva0d0tjd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.06355",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "3203|Suicide and Self-Harm Studies"
    ]
  ],
  "openalex": "W7220699983",
  "pdf_url": "https://arxiv.org/pdf/2610.06355",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/03wqmva0d0tjd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:16Z",
      "native_id": "2610.06355",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.06355"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support",
  "title_text": "Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:16Z",
  "url": "https://research.draconitic.com/paper/03wqmva0d0tjd",
  "venue": "arXiv",
  "versions": []
}