ArticleInternational journal of methods in psychiatric research2018
Automatic mining of symptom severity from psychiatric evaluation notes.
Article in International journal of methods in psychiatric research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled it
- Leveraging artificial intelligence to personalize treatment for eating disorders: A proof-of-concept study.Journal of affective disorders · 2026Article
- Artificial Intelligence in Orofacial Pain: Diagnostic and Predictive Performance Across Machine Learning and Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Review
- Machine learning in the analysis of mental health at work: a scoping review.Journal of occupational health · 2026Article
- Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023.Heliyon · 2024Review
- Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.Communications medicine · 2024Article
- Comparative Merits of Available Mortality Data Sources for Clinical Research.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Predicting COVID-19 Symptoms From Free Text in Medical Records Using Artificial Intelligence: Feasibility Study.JMIR medical informatics · 2022Article
- Development of an Innovative Digital Data Collection System for Routine Mental Health Care Delivery in Rural Haiti.Global health, science and practice · 2021Article
- An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain.The Japanese dental science review · 2021Review
- Contextual embedding bootstrapped neural network for medical information extraction of coronary artery disease records.Medical & biological engineering & computing · 2021Article
- Text Mining of Electronic Health Records Can Accurately Identify and Characterize Patients With Systemic Lupus Erythematosus.ACR open rheumatology · 2021Article
- Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study.Journal of medical Internet research · 2020Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Building an Automated Orofacial Pain, Headache and Temporomandibular Disorder Diagnosis System.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- A rule-based approach to identify patient eligibility criteria for clinical trials from narrative longitudinal records.JAMIA open · 2019Article
- Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study.Journal of medical Internet research · 2018Article
- Automatic mining of symptom severity from psychiatric evaluation notes.International journal of methods in psychiatric research · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectivesAs electronic mental health records become more widely available, several approaches have been suggested to automatically extract information from free-text narrative aiming to support epidemiological research and clinical decision-making. In this paper, we explore extraction of explicit mentions of symptom severity from initial psychiatric evaluation records. We use the data provided by the 2016 CEGS N-GRID NLP shared task Track 2, which contains 541 records manually annotated for symptom severity according to the Research Domain Criteria.
methodsWe designed and implemented 3 automatic methods: a knowledge-driven approach relying on local lexicalized rules based on common syntactic patterns in text suggesting positive valence symptoms; a machine learning method using a neural network; and a hybrid approach combining the first 2 methods with a neural network.
resultsThe results on an unseen evaluation set of 216 psychiatric evaluation records showed a performance of 80.1% for the rule-based method, 73.3% for the machine-learning approach, and 72.0% for the hybrid one.
conclusionsAlthough more work is needed to improve the accuracy, the results are encouraging and indicate that automated text mining methods can be used to classify mental health symptom severity from free text psychiatric notes to support epidemiological and clinical research.
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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.