ArticleCell reports. Medicine2023
Combining clinical notes with structured electronic health records enhances the prediction of mental health crises.
Article in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- PHO-Agents: A Large Language Model-Powered Multi-Agent System for Predicting Health Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Predicting Laboratory Test Ordering in Emergency Departments Using Integrated Structured and Unstructured Electronic Health Records: Machine Learning Study.JMIR medical informatics · 2026Article
- Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications.JAMA network open · 2026Article
- Life events extraction from healthcare notes for veteran acute suicide risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A Systematic Review of Topic Modeling Techniques for Electronic Health Records.Healthcare (Basel, Switzerland) · 2026Review
- Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- A novel interpretable machine learning framework for predicting postpartum depression: a SHAP-based analysis of maternal and infant health indicators.Frontiers in psychiatry · 2026Article
- Multiview deep-learning-enabled histopathology for prognostic and therapeutic stratification in stage II colorectal cancer: A retrospective multicenter study.PLoS medicine · 2026Observational
- Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction.Advances in neural information processing systems · 2025Article
- Analyzing Information Disparities across Modalities in Mortality Prediction.medRxiv : the preprint server for health sciences · 2025Article
- A Data-Driven Framework for Digital Transformation in Smart Cities: Integrating AI, Dashboards, and IoT Readiness.Sensors (Basel, Switzerland) · 2025Article
- Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study.Journal of medical Internet research · 2025Observational
- Perceptions of electronic health records by ethnically diverse groups in mental health: A systematic literature review.Global mental health (Cambridge, England) · 2025Review
- Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises.NPJ digital medicine · 2024Article
- An explainable long short-term memory network for surgical site infection identification.Surgery · 2024Article
- Classifying early infant feeding status from clinical notes using natural language processing and machine learning.Scientific reports · 2024Article
- Individualized post-crisis monitoring of psychiatric patients via Hidden Markov models.Frontiers in digital health · 2024Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An automatic prediction of mental health crises can improve caseload prioritization and enable preventative interventions, improving patient outcomes and reducing costs. We combine structured electronic health records (EHRs) with clinical notes from 59,750 de-identified patients to predict the risk of mental health crisis relapse within the next 28 days. The results suggest that an ensemble machine learning model that relies on structured EHRs and clinical notes when available, and relying solely on structured data when the notes are unavailable, offers superior performance over models trained with either of the two data streams alone. Furthermore, the study provides key takeaways related to the required amount of clinical notes to add value in predictive analytics. This study sheds light on the untapped potential of clinical notes in the prediction of mental health crises and highlights the importance of choosing an appropriate machine learning method to combine structured and unstructured EHRs.
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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.