Inferring cancer disease response from radiology reports using large language models with data augmentation and prompting.

Ryan Shea Ying Cong Tan,Qian Lin, Guat Hwa Low,Ruixi Lin, Tzer Chew Goh, Christopher Chu En Chang, Fung Fung Lee, Wei Yin Chan, Wei Chong Tan,Han Jieh Tey, Fun Loon Leong,Hong Qi Tan,Wen Long Nei,Wen Yee Chay,David Wai Meng Tai,Gillianne Geet Yi Lai,Lionel Tim-Ee Cheng,Fuh Yong Wong, Matthew Chin Heng Chua,Melvin Lee Kiang Chua,Daniel Shao Weng Tan,Choon Hua Thng,Iain Bee Huat Tan,Hwee Tou Ng

Journal of the American Medical Informatics Association : JAMIA（2023）

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摘要

Large clinical language models demonstrate potential to infer cancer disease response from radiology reports at scale. Data augmentation techniques are useful to further improve performance. Prompt-based fine-tuning can significantly reduce the size of the training dataset.

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关键词

cancer disease response,radiology reports,large language models,data augmentation

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