Large Language Models are Few-Shot Clinical Information Extractors
This work addresses the challenge of clinical NLP for healthcare professionals by enabling efficient information extraction without extensive domain-specific training, though it is incremental as it applies existing models to a new domain.
The paper tackled the problem of extracting structured information from clinical notes by demonstrating that large language models like InstructGPT perform well at zero- and few-shot clinical information extraction, significantly outperforming existing baselines with concrete improvements in tasks such as span identification and relation extraction.
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, 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 for new tasks. On the clinical extraction tasks we studied, the GPT-3 systems significantly outperform existing zero- and few-shot baselines.