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Large language models (LLMs), such as GPT-4, have demonstrated remarkable capabilities across a wide range of tasks, including health applications.
Adverse drug events in hospitalized patients: excess length of stay, extra costs, and attributable mortality
David C Classen, Stanley L Pestotnik, R Scott Evans, James F Lloyd, and John P Burke · 1997
Earlier work this paper cites.
To err is human: building a safer health system
Molla S Donaldson, Janet M Corrigan, Linda T Kohn, et al · 2000
Earlier work this paper cites.
The genetic association database
Kevin G Becker, Kathleen C Barnes, Tami J Bright, S Hong Wang, and The Genetic Association Information Network · 2004
Earlier work this paper cites.
Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports
Harsha Gurulingappa, Abdul Mateen Rajput, Angus Roberts, Juliane Fluck, Martin Hofmann-Apitius, and Luca Toldo · 2012
Earlier work this paper cites.
2014 i2b2/uthealth shared task on diagnosis and procedure coding for clinical text
Özlem Uzuner, Brett R South, Shuying Shen, and Scott L DuVall · 2014
Earlier work this paper cites.
Lessons from natural language inference in the clinical domain
Alexey Romanov and Chaitanya Shivade · 2018
Earlier work this paper cites.
Span-based joint entity and relation extraction with transformer pre-training
Markus Eberts and Adrian Ulges · 2019
Earlier work this paper cites.
Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning
Long Chen, Yu Gu, Xin Ji, Zhiyong Sun, Haodan Li, Yuan Gao, and Yang Huang · 2020
Earlier work this paper cites.
2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records
Sam Henry, Kevin Buchan, Michele Filannino, Amber Stubbs, and Ozlem Uzuner · 2020
Cited alongside, same era.
Two are better than one: Joint entity and relation extraction with table-sequence encoders
Jue Wang and Wei Lu · 2020
Cited alongside, same era.
Rebel: Relation extraction by end-to-end language generation
Pere-Lluís Huguet Cabot and Roberto Navigli · 2021
Cited alongside, same era.
Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Thinking about gpt-3 in-context learning for biomedical ie? think again
Bernal Jiménez Gutiérrez, Nikolas McNeal, Clay Washington, You Chen, Lang Li, Huan Sun, and Yu Su · 2022
Later among the works it cites.
Biogpt: generative pre-trained transformer for biomedical text generation and mining
Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, and Tie-Yan Liu · 2022
Later among the works it cites.
Gpt-3 models are poor few-shot learners in the biomedical domain, 2022
Milad Moradi, Kathrin Blagec, Florian Haberl, and Matthias Samwald · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Later among the works it cites.
Language models in the loop: Incorporating prompting into weak supervision, 2022
Ryan Smith, Jason A. Fries, Braden Hancock, and Stephen H. Bach · 2022
Later among the works it cites.
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Joint entity and relation extraction from scientific documents: role of linguistic information and entity types
TYSS Santosh, Prantika Chakraborty, Sudakshina Dutta, Debarshi Kumar Sanyal, and Partha Pratim Das · 2021
Cited alongside, same era.
Fine-tuning large neural language models for biomedical natural language processing, 2021
Robert Tinn, Hao Cheng, Yu Gu, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
Cited alongside, same era.
Large language models are few-shot clinical information extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag · 2022
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4, 2023
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang · 2023
Closest in time.
The AI Revolution in Medicine: GPT-4 and Beyond
Peter Lee, Carey Goldberg, and Isaac Kohane · 2023
Closest in time.
Gpt-4 technical report, 2023
OpenAI · 2023
Closest in time.