ArticleMayo Clinic proceedings. Digital health2023
Building a Natural Language Processing Artificial Intelligence to Predict Suicide-Related Events Based on Patient Portal Message Data.
Article in Mayo Clinic proceedings. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Large language model-based triage to identify antiretroviral therapy adherence barriers and risk levels in patient messages.JAMIA open · 2026Article
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Enhancing personalized suicide risk prediction for VA patients by integrating discrete natural language processing models.Translational psychiatry · 2026Article
- Predicting imminent suicide risk in a crisis hotline chat using machine learning.Scientific reports · 2025Article
- Applying natural language processing to patient messages to identify depression concerns in cancer patients.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- As Suicide Prediction With Artificial Intelligence Moves Forward, Barriers to Implementation Remain.Mayo Clinic proceedings. Digital health · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop a natural language processing artificial intelligence model trained on text from patient portal messages to predict 30-day suicide-related events (SRE). Patients and Methods: Patient portal messages sent by patients between January 1, 2013, and October 31, 2017 were screened for an associated SRE within 30 days. For both patient portal messages associated with a 30-day SRE and a randomized control set, we automatically extracted several features: (1) frequencies of keywords; (2) message metadata; and (3) message sentiment. Results: A total of 840 patient portal messages were included in our final analysis, including 420 messages with and without an associated 30-day SRE. Patient messages with an associated 30-day SRE had a mean sentiment score that was less than those without an SRE ( Conclusion: A natural language processing artificial intelligence model trained on a subset of patient portal message data was able to predict 30-day SRE at a level comparable to commonly used suicide assessment tools. Predictors that conveyed the overall tone of a patient message, such as the sentiment score, were more highly weighted by machine learning models in predicting 30-day SRE than the frequencies of individual words.
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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.