Large language models are few-shot clinical information extractors

Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David Sontag


Abstract
A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the general domain and a lack of public clinical corpora and annotations. In this work, we show that large language models, such as InstructGPT (Ouyang et al., 2022), perform well at zero- and few-shot information extraction from clinical text despite not being trained specifically for the clinical domain. Whereas text classification and generation performance have already been studied extensively in such models, here we additionally demonstrate how to leverage them to tackle a diverse set of NLP tasks which require more structured outputs, including span identification, token-level sequence classification, and relation extraction. Further, due to the dearth of available data to evaluate these systems, we introduce new datasets for benchmarking few-shot clinical information extraction based on a manual re-annotation of the CASI dataset (Moon et al., 2014) for new tasks. On the clinical extraction tasks we studied, the GPT-3 systems significantly outperform existing zero- and few-shot baselines.
Anthology ID:
2022.emnlp-main.130
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
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Publisher:
Association for Computational Linguistics
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Pages:
1998–2022
Language:
URL:
https://aclanthology.org/2022.emnlp-main.130
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Cite (ACL):
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag. 2022. Large language models are few-shot clinical information extractors. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1998–2022, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Large language models are few-shot clinical information extractors (Agrawal et al., EMNLP 2022)
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https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.130.pdf