Identifying Key Predictive Variables in Medical Records Using a Large Language Model (LLM)

Abstract EHR systems are widely used, but leveraging their unstructured clinical notes for insights has been challenging. Large Language Models (LLMs) can offer scalable, precise extraction of pertinent information from clinical notes. This paper presents a novel framework for using LLMs to derive medical insights from EHRs, demonstrated through an assessment on female infertility within the Veterans Health Administration (VHA), combining unstructured and structured data for enhanced analysis.