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

ABSTRACT:

Background

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 integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable.

Objective

This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs).

Methods

Using a dataset from the EHR systems of more than 50 health care provider org

SUBMITTER: Cardamone NC 

PROVIDER: S-EPMC11884378 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature