TY - GEN
T1 - Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning
AU - Ramachandran, Giridhar Kaushik
AU - Fu, Yujuan
AU - Han, Bin
AU - Lybarger, Kevin
AU - Dobbins, Nicholas J.
AU - Uzuner, Özlem
AU - Yetisgen, Meliha
N1 - Publisher Copyright:
© 2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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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M3 - Conference contribution
AN - SCOPUS:85175428586
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 385
EP - 393
BT - 5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023 - Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023
Y2 - 14 July 2023
ER -