Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning

• URL: http://arxiv.org/abs/2306.07170v1
• Date: Mon, 12 Jun 2023 15:08:25 GMT
• Title: Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning
• Authors: Giridhar Kaushik Ramachandran, Yujuan Fu, Bin Han, Kevin Lybarger, Nicholas J Dobbins, \"Ozlem Uzuner, Meliha Yetisgen
• Abstract summary: Social determinants of health (SDOH) documented in the electronic health record are being studied to understand how SDOH impacts patient health outcomes. In this work, we utilize the Social History 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. Our prompt-based GPT-4 method achieved an overall 0.652 F1 on the SHAC test set,
• Score: 3.418600863629033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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