@inproceedings{ramachandran-etal-2023-prompt,
title = "Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning",
author = "Ramachandran, Giridhar Kaushik and
Fu, Yujuan and
Han, Bin and
Lybarger, Kevin and
Dobbins, Nic and
Uzuner, Ozlem and
Yetisgen, Meliha",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Rumshisky, Anna",
booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.clinicalnlp-1.41",
doi = "10.18653/v1/2023.clinicalnlp-1.41",
pages = "385--393",
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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%0 Conference Proceedings
%T Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning
%A Ramachandran, Giridhar Kaushik
%A Fu, Yujuan
%A Han, Bin
%A Lybarger, Kevin
%A Dobbins, Nic
%A Uzuner, Ozlem
%A Yetisgen, Meliha
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ramachandran-etal-2023-prompt
%X 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.
%R 10.18653/v1/2023.clinicalnlp-1.41
%U https://aclanthology.org/2023.clinicalnlp-1.41
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.41
%P 385-393
Markdown (Informal)
[Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning](https://aclanthology.org/2023.clinicalnlp-1.41) (Ramachandran et al., ClinicalNLP 2023)
ACL