{
  "abstract": "Objective: Suicidal ideation (SI) is a critical clinical concern, yet most predictive approaches rely on static or aggregate measures that overlook its session-to-session dynamics. This study aimed to improve next-session SI prediction by integrating dynamic item-level data and identifying the clinical features that contribute most strongly throughout treatment. Methods: Data from 2,045 outpatients were analyzed across the first 10 sessions. A transformer-based time-series model was trained to predict next-session SI using prior-session item-level scores and was compared against a static elastic-net baseline. SHAP values were calculated to quantify feature contributions at both group and individual levels. Results: The dynamic model significantly outperformed the static baseline (average R² = .95 vs. .12), and performance remained improved even without autoregressive inputs (average R² = .24). While prior-session SI was consistently the strongest predictor, depressive affect, hopelessness, meaning in life, and anxiety-related symptoms also provided significant predictive value. SHAP analyses further revealed temporal fluctuations and patient-specific patterns in influential features. Conclusion: Dynamic, interpretable models using item-level data substantially enhance short-term SI prediction. These findings provide clinical insights into which symptoms are most relevant for specific patients at different times, supporting more responsive and personalized suicide risk monitoring.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Juan Segundo Peña Loray"
    },
    {
      "name": "Julian Rubel"
    },
    {
      "name": "Juan Martín Gómez Penedo"
    },
    {
      "name": "Miriam Hehlmann"
    },
    {
      "name": "Tobias Steinbrenner"
    },
    {
      "name": "Jay Lawrynowicz"
    },
    {
      "name": "Wolfgang Lutz"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6kdmzsf0jcxys.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/vk4pw_v2",
  "fulltext_url": null,
  "id": "6kdmzsf0jcxys",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/vk4pw_v2/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Clinical Psychology"
    ],
    [
      "bepress",
      "Life Sciences"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "openalex",
      "3203|Suicide and Self-Harm Studies"
    ]
  ],
  "openalex": "W7213516137",
  "pdf_url": "https://osf.io/vk4pw/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6kdmzsf0jcxys.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:33Z",
      "native_id": "vk4pw",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/vk4pw_v2/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Life Sciences",
      "subfield": "general"
    },
    {
      "field": "medicine",
      "origin": "bepress:Clinical Psychology",
      "subfield": "psychiatry"
    }
  ],
  "title": "Timing Is Everything: Modeling the Temporal Dynamics of Suicidal Ideation",
  "title_text": "Timing Is Everything: Modeling the Temporal Dynamics of Suicidal Ideation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:33Z",
  "url": "https://research.draconitic.com/paper/6kdmzsf0jcxys",
  "venue": "PsyArXiv",
  "versions": []
}