ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 10 citations in OpenAlex.
- A qualitative interview study investigating patient, health professional, and developer perspectives on real-world implementation of patient-centered AI systems.NPJ digital medicine · 2026Article
- A qualitative Interview Study Investigating Patient, Health Professional, and Developer Perspectives on Real-World Implementation of Patient-Centered AI Systems.Research square · 2025Article
- Exploring the Perspectives of Pediatric Health Care Providers, Youth Patients, and Caregivers on Machine Learning Suicide Risk Classification: Mixed Methods Study.Journal of medical Internet research · 2025Article
- Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study.JMIR mental health · 2024Article
- External Validation and Updating of a Statistical Civilian-Based Suicide Risk Model in US Naval Primary Care.JAMA network open · 2023Article
- Human-Centered Design to Address Biases in Artificial Intelligence.Journal of medical Internet research · 2023Article
- Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency.International journal of methods in psychiatric research · 2022Observational
- Trust in AI: why we should be designing for APPROPRIATE reliance.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Risky business: a scoping review for communicating results of predictive models between providers and patients.JAMIA open · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, we are developing an intervention that uses predictive analytics to inform care teams about their patients' risk of suicide attempt. We present our experience working with clinicians and staff in a military primary care setting to create preliminary designs and a context-specific usability testing plan for the deployment of the suicide risk indicator.
Indexed as
Identifiers
33936481PMC8075431W3157477022What OpenQuestion holds
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.