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Identifying and Extracting Rare Diseases and Their Phenotypes with Large Language Models.

Cathy ShyrYan HuLisa BastaracheAlex ChengRizwan HamidPaul HarrisHua Xu
Published in: Journal of healthcare informatics research (2024)
Prompt learning using ChatGPT has the potential to match or outperform fine-tuning BioClinicalBERT at extracting rare diseases and signs with just one annotated sample. Given its accessibility, ChatGPT could be leveraged to extract these entities without relying on a large, annotated corpus. While LLMs can support rare disease phenotyping, researchers should critically evaluate model outputs to ensure phenotyping accuracy.
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