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.

本文使用 SHAC 数据集中的社交史部分，采用 GPT-4 的一次性提示設置探索 SDOH 信息的自动提取，其中包括物質使用，就业和生活状况信息，和采用高性能监督方法将 GPT-4 提取性能与其进行比较，并进行彻底的错误分析。GPT-4 的提示方法在 SHAC 测试中获得了总体 0.652 的 F1，类似于 n2c2 挑战中最好的 7 个系统之一