Extracting Social Determinants of Health Information from Clinical Notes Using Large Language Models

Presentation Time: 11:45 AM - 12:00 PM

Abstract Keywords: Diversity, Equity, Inclusion, Accessibility, and Health Equity, Large Language Models (LLMs), Information Extraction, Natural Language Processing, Data Mining, Health Equity
Primary Track: Applications

Our work investigates the potential of large language models (LLMs) in extracting and categorizing social determinants of health (SDOH) from clinical notes. MIMIC dataset analysis revealed low SDOH ICD code utilization, yet significant SDOH details in clinical notes through NLP application. Given the potential for extracting SDOH from free-text, challenges unraveled using Llama-2 compared to previous benchmarks. This research emphasizes needed methodological refinement and annotated datasets to advance SDOH integration in healthcare practice and research.

Speaker(s):
Tim Schwirtlich, PhD
Northwestern University

Author(s):
Tim Schwirtlich, PhD - Northwestern University; Vivian Pan, MS, CGC - University of Illinois; Sara Muhammad, MS - University of Illinois Chicago; Saki Amagai - Northwestern University; Yuan Luo, PhD - Northwestern University;