ArticlePsychological medicine2021
Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.
Article in Psychological medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 3 syntheses or guidelines pooled it, 89 citations in OpenAlex.
- Artificial intelligence in healthcare: transforming patient safety with intelligent systems-A systematic review.Frontiers in medicine · 2024Pooled it
- Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- The Role of Artificial Intelligence in Clinical Psychology: How AI and NLP Systems Are Reshaping Psychological Interventions. A Systematic Review.Clinical psychology & psychotherapyPooled it
- Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans.medRxiv : the preprint server for health sciences · 2026Article
- Personalizing Suicide Risk Assessment: Machine Learning Extraction of Cross-Modal Interactions Between Psychosocial and Demographic Factors in Veterans.medRxiv : the preprint server for health sciences · 2026Article
- Life events extraction from healthcare notes for veteran acute suicide risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Enhancing personalized suicide risk prediction for VA patients by integrating discrete natural language processing models.Translational psychiatry · 2026Article
- Developing a Natural Language Processing Strategy to Avoid Biased Data in Electronic Health Record Suicide Risk Modeling.Psychiatric research and clinical practice · 2026Article
- Article
- Sentiment Analysis Using a Large Language Model-Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation.JMIR infodemiology · 2025Article
- A paper-and-pencil questionnaire outperforms GPT for measuring cognitive vulnerability to depression and predicting depressive symptoms.Scientific reports · 2025Article
- Preprocessing of natural language process variables using a data-driven method improves the association with suicide risk in a large veterans affairs population.Computers in biology and medicine · 2025Article
- ND-AMD: A Web-Based Database for Animal Models of Neurological Disease With Analysis Tools.CNS neuroscience & therapeutics · 2025Article
- Evolution of Linguistic Markers of Agency, Centrality and Content During Metacognitive Therapy for Psychosis: A Pilot Exploratory Study.Early intervention in psychiatry · 2025Article
- INSIGHTFUL: insight generation through clinical annotation, analysis and modelling of suicide-related factors towards understanding and lifesaving.BMJ digital health & AI · 2025Article
- Using Natural Language Processing to Inform Targeted Rural and Urban Hispanic VA Suicide Prediction Models.Rural mental health · 2025Article
- Using natural language processing to explore differences in healthcare professionals' language on Functional Neurological Disorder: a comparative topic and sentiment analysis study.Frontiers in digital health · 2025Article
- Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Enhancing Suicide Attempt Risk Prediction Models with Temporal Clinical Note Features.Applied clinical informatics · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThis study evaluated whether natural language processing (NLP) of psychotherapy note text provides additional accuracy over and above currently used suicide prediction models.
methodsWe used a cohort of Veterans Health Administration (VHA) users diagnosed with post-traumatic stress disorder (PTSD) between 2004-2013. Using a case-control design, cases (those that died by suicide during the year following diagnosis) were matched to controls (those that remained alive). After selecting conditional matches based on having shared mental health providers, we chose controls using a 5:1 nearest-neighbor propensity match based on the VHA's structured Electronic Medical Records (EMR)-based suicide prediction model. For cases, psychotherapist notes were collected from diagnosis until death. For controls, psychotherapist notes were collected from diagnosis until matched case's date of death. After ensuring similar numbers of notes, the final sample included 246 cases and 986 controls. Notes were analyzed using Sentiment Analysis and Cognition Engine, a Python-based NLP package. The output was evaluated using machine-learning algorithms. The area under the curve (AUC) was calculated to determine models' predictive accuracy.
resultsNLP derived variables offered small but significant predictive improvement (AUC = 0.58) for patients that had longer treatment duration. A small sample size limited predictive accuracy.
conclusionsStudy identifies a novel method for measuring suicide risk over time and potentially categorizing patient subgroups with distinct risk sensitivities. Findings suggest leveraging NLP derived variables from psychotherapy notes offers an additional predictive value over and above the VHA's state-of-the-art structured EMR-based suicide prediction model. Replication with a larger non-PTSD specific sample is required.
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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.