Prompt-based Extraction of Social Determinants of Health Using Few-shot
  Learning
Giridhar Kaushik Ramachandran Yujuan Fu Bin Han Kevin Lybarger Nicholas J Dobbins Özlem Uzuner Meliha Yetisgen
Social determinants of health (SDOH) documented in the electronic health
record through unstructured text are increasingly being studied to understand
how SDOH impacts patient health outcomes. In this work, we utilize the Social
History Annotation Corpus (SHAC), a multi-institutional corpus of de-identified
social history sections annotated for SDOH, including substance use,
employment, and living status information. We explore the automatic extraction
of SDOH information with SHAC in both standoff and inline annotation formats
using GPT-4 in a one-shot prompting setting. We compare GPT-4 extraction
performance with a high-performing supervised approach and perform thorough
error analyses. Our prompt-based GPT-4 method achieved an overall 0.652 F1 on
the SHAC test set, similar to the 7th best-performing system among all teams in
the n2c2 challenge with SHAC.