ArticleJournal of psychiatric research2021
Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation.
Article in Journal of psychiatric research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Computable Phenotype for Identifying Undiagnosed Hypermobile Ehlers-Danlos Syndrome: Protocol for a Development and Validation Study.JMIR research protocols · 2026Article
- Weak-Supervision Expectation-Maximization Framework for Identifying Decisional Vulnerability in Older Emergency Department Patients.Healthcare (Basel, Switzerland) · 2026Article
- The Effects of Artificial Intelligence in Quality Assessment of Outpatient Mental Health Services on Human Auditors.Applied clinical informatics · 2026Article
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing.Scientific reports · 2026Article
- Scalable Identification of Clinically Relevant Chronic Obstructive Pulmonary Disease Documents in Large-Scale Electronic Health Record Datasets With a Lightweight Natural Language Processing Model: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Developing a Suicide Risk Prediction Algorithm Using Electronic Health Record Data in Mental Health Care: Real-World Case Study.JMIR medical informatics · 2026Article
- A method to enable clinical and translational research teams with custom real-world data from electronic health record systems.Journal of clinical and translational science · 2026Article
- Natural language processing for suicidal behavior detection in short Spanish clinical notes from first-contact interviews.Frontiers in neuroinformatics · 2026Article
- Large language models in adolescent suicide prevention: from language signals to accountable action.Frontiers in public health · 2026Review
- Article
- Using Natural Language Processing Methods to Predict Topics Included in 2019 Ohio Syphilis Disease Intervention Specialist Records.Sexually transmitted diseases · 2025Article
- Leveraging large language models for knowledge-free weak supervision in clinical natural language processing.Scientific reports · 2025Article
- Validation of a machine learning model for indirect screening of suicidal ideation in the general population.Scientific reports · 2025Article
- Post-discharge suicide prediction among US veterans using natural language processing-enriched social and behavioral determinants of health.Npj mental health research · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- Automated detection and prediction of suicidal behavior from clinical notes using deep learning.PloS one · 2025Article
- Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- The Goldilocks Zone: Finding the right balance of user and institutional risk for suicide-related generative AI queries.PLOS digital health · 2025Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Mental health concerns, such as suicidal thoughts, are frequently documented by providers in clinical notes, as opposed to structured coded data. In this study, we evaluated weakly supervised methods for detecting "current" suicidal ideation from unstructured clinical notes in electronic health record (EHR) systems. Weakly supervised machine learning methods leverage imperfect labels for training, alleviating the burden of creating a large manually annotated dataset. After identifying a cohort of 600 patients at risk for suicidal ideation, we used a rule-based natural language processing approach (NLP) approach to label the training and validation notes (n = 17,978). Using this large corpus of clinical notes, we trained several statistical machine learning models-logistic classifier, support vector machines (SVM), Naive Bayes classifier-and one deep learning model, namely a text classification convolutional neural network (CNN), to be evaluated on a manually-reviewed test set (n = 837). The CNN model outperformed all other methods, achieving an overall accuracy of 94% and a F1-score of 0.82 on documents with "current" suicidal ideation. This algorithm correctly identified an additional 42 encounters and 9 patients indicative of suicidal ideation but missing a structured diagnosis code. When applied to a random subset of 5,000 clinical notes, the algorithm classified 0.46% (n = 23) for "current" suicidal ideation, of which 87% were truly indicative via manual review. Implementation of this approach for large-scale document screening may play an important role in point-of-care clinical information systems for targeted suicide prevention interventions and improve research on the pathways from ideation to attempt.
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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.