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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023 - Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages385-393
Number of pages9
ISBN (Electronic)9781959429883
StatePublished - 2023
Externally publishedYes
Event5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023 - Toronto, Canada
Duration: Jul 14 2023 → …

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023
Country/TerritoryCanada
CityToronto
Period7/14/23 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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