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Inferring cancer disease response from radiology reports using large language models with data augmentation and prompting
38
Zitationen
24
Autoren
2023
Jahr
Abstract
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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Autoren
- Ryan 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 Lai
- Lionel Tim‐Ee Cheng
- Fuh Yong Wong
- Matthew Chin Heng Chua
- Melvin L.K. Chua
- Daniel S.W. Tan
- Choon Hua Thng
- Iain Bee Huat Tan
- Hwee Tou Ng