ArticleNpj mental health research2024
Developing a suicide risk model for use in the Indian Health Service.
Article in Npj mental health research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06782516 (Native-RISE), which is not on this map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Native-RISE (Risk Identification for Suicide and Enhanced Care)
Who cites it
5 citing papers in PubMed.
- A Guide to Constructing Indigenous Statistical Spaces for Prevention Science Research.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Reclaiming Data, Restoring Health: The Indigenous Biomedical Data Science Renaissance.Annual review of biomedical data science · 2026Review
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study.Journal of medical Internet research · 2026Observational
- AI-Y: An AI Checklist for Population Ethics Across the Global Context.Current epidemiology reports · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
We developed and evaluated an electronic health record (EHR)-based model for suicide risk specific to an American Indian patient population. Using EHR data for all patients over 18 with a visit between 1/1/2017 and 10/2/2021, we developed a model for the risk of a suicide attempt or death in the 90 days following a visit. Features included demographics, medications, diagnoses, and scores from relevant screening tools. We compared the predictive performance of logistic regression and random forest models against existing suicide screening, which was augmented to include the history of previous attempts or ideation. During the study, 16,835 patients had 331,588 visits, with 490 attempts and 37 deaths by suicide. The logistic regression and random forest models (area under the ROC (AUROC) 0.83 [0.80-0.86]; both models) performed better than enhanced screening (AUROC 0.64 [0.61-0.67]). These results suggest that an EHR-based suicide risk model can add value to existing practices at Indian Health Service clinics.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.