Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study

• Published in: JMIR medical informatics Vol. 13; p. e65454
• Main Authors: Cardamone, Nicholas C, Olfson, Mark, Schmutte, Timothy, Ungar, Lyle, Liu, Tony, Cullen, Sara W, Williams, Nathaniel J, Marcus, Steven C
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 21.01.2025
• Subjects: AI Language Models in Health Care; Anxiety; Artificial intelligence; Chronic fatigue syndrome; Classification; Data warehouses; Datasets; Decision Support for Health Professionals; Diagnostic Tools in Mental Health; Disease; Eating disorders; Electronic Health Records; Electronic Health Records - statistics & numerical data; Emergency Service, Hospital; Emotional disorders; Humans; Impulsivity; Large Language Models; Machine Learning; Medical coding; Mental disorders; Mental Disorders - diagnosis; Mental Health; Methods and New Tools in Mental Health Research; Mood disorders; Multimedia; Natural Language Processing; New Technologies; Original Paper; Personality disorders; Psychosis; Reconciliation; Review boards; Self destructive behavior; United States; United States; natural language processing; mental health; AI; mental health disorder; LLM; emergency department; large language model; machine learning; EHR; EHR system; artificial intelligence; NLP; physical health; ChatGPT; predictive modeling; health informatics; text; dataset; electronic health record; ML

Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrate...