ChatGPT-4 extraction of heart failure symptoms and signs from electronic health records.

ABSTRACT:

Background

Natural language processing (NLP) can facilitate research utilizing data from electronic health records (EHRs). Large language models can potentially improve NLP applications leveraging EHR notes. The objective of this study was to assess the performance of zero-shot learning using Chat Generative Pre-trained Transformer 4 (ChatGPT-4) for extraction of symptoms and signs, and compare its performance to baseline machine learning and rule-based methods developed using annotated data.

Methods and results

From unstructured clinical notes of the national EHR data of the Veterans healthcare system, we extracted 1999 text snippets containing relevant keywords for heart failure symptoms and signs, which were then annotated by two clinicians. We also created 102 synthetic

SUBMITTER: Workman TE 

PROVIDER: S-EPMC12844218 | biostudies-literature | 2024 Nov-Dec

REPOSITORIES: biostudies-literature