Abstract
This Viewpoint argues that suicide risk prediction models are advancing faster than the shared infrastructure health systems need to evaluate, select, and sustain them and proposes a federated framework for closing that gap.
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Privacy-aware community infrastructureYunyu Xiao, Colin G. Walsh, Hadi H.K. Kharrazi, J. John Mann
Abstract
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Predicting suicide attempts and suicide deaths following outpatient visits using electronic health records.
10.1176/appi.ajp.2018.17101167 · 2018
Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts.
10.1093/jamiaopen/ooab011 · 2021
Prediction of fatal and non-fatal suicide attempts by the Columbia Suicide Severity Rating Scale (C-SSRS): systematic review and meta-analysis.
10.1192/bjp.2025.10316 · 2025
Integration of face-to-face screening with real-time machine learning to predict risk of suicide among adults.
10.1001/jamanetworkopen.2022.12095 · 2022
Prediction models for self-harm and suicide: a systematic review and critical appraisal.
10.1186/s12916-025-04367-6 · 2025
Suicide death prediction using the Maryland Suicide Data Warehouse: a sensitivity analysis.
10.1080/13811118.2024.2363227 · 2025
Patterns of social determinants of health and child mental health, cognition, and physical health.
10.1001/jamapediatrics.2023.4218 · 2023
Machine learning to investigate policy-relevant social determinants of health and suicide rates in the United States.
10.1038/s44220-025-00424-4 · 2025
Provenance
crossref
Confidence 100%
pubmed
Confidence 98%
europepmc
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unpaywall
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openalex
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datacite
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Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework.
10.1093/jamia/ocad075 · 2023
Implementing cardiovascular risk prediction in clinical practice: the future is now.
10.1161/jaha.117.006019 · 2017
No additional external references are available.