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Hazard-aware adaptations bridge the generalization gap in large language models: a nationwide study
2025-02-17
health informatics
Title + abstract only
View on medRxiv
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Despite growing excitement in deploying large language models (LLMs) for healthcare, most machine learning studies show success on the same few limited public data sources. It is unclear if and how most results generalize to real-world clinical settings. To measure this gap and shorten it, we analyzed protected notes from over 100 Veterans Affairs (VA) sites, focusing on extracting smoking history--a persistent and clinically impactful problem in natural language processing (NLP). Here we applie...
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