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Many diagnostic errors occur because clinicians cannot easily access relevant information in patient Electronic Health Records (EHRs).
What’s the relative risk?: A method of correcting the odds ratio in cohort studies of common outcomes
Jun Zhang and F Yu Kai. 1998 · 1998
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Understanding the odds ratio and the relative risk
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Thomas Searle, Zina Ibrahim, and Richard JB Dobson. 2020 · 2006
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Patient Record Review of the Incidence, Consequences, and Causes of Diagnostic Adverse Events
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Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review
Benjamin A Goldstein, Ann Marie Navar, Michael J Pencina, and John P A Ioannidis. 2016 · 2016
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Publicly available clinical BERT embeddings
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Hi-behrt: Hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records
Yikuan Li, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Shishir Rao, Abdelaali Hassaine, Dexter Canoy, Thomas Lukasiewicz, and Kazem Rahimi. 2021 · 2021
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Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction
Finetuned language models are zero-shot learners
Jason Wei, Maarten Paul Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew Mingbo Dai, and Quoc V. Le. 2022 · 2022
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Retrieving evidence from ehrs with llms: Possibilities and challenges
Hiba Ahsan, Denis Jered McInerney, Jisoo Kim, Christopher Potter, Geoffrey Young, Silvio Amir, and Byron C. Wallace. 2023 · 2023
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Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models
Emily Alsentzer, Mary-Jette Rasmussen, Raíssa Schmitt Fontoura, Andrew Cull, Brett K. Beaulieu-Jones, Kathryn J. Gray, D. Bates, and Vesela P. Kovacheva. 2023 · 2023
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A comparative study of pretrained language models for long clinical text
Yikuan Li, Ramsey M Wehbe, Faraz S Ahmad, Hanyin Wang, and Yuan Luo. 2023 · 2023
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CHiLL: Zero-shot custom interpretable feature extraction from clinical notes with large language models
Denis McInerney, Geoffrey Young, Jan-Willem van de Meent, and Byron Wallace. 2023 · 2023
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Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, and Degui Zhi. 2021 · 2021
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Large language models are few-shot clinical information extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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MIMIC-III clinical database (version 1.4)
Alistair E. W. Johnson, Tom J. Pollard, and Roger G. Mark. 2016a
Cited in the paper.
MIMIC-III, a freely accessible critical care database
Alistair E.W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark. 2016b
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Burden of serious harms from diagnostic error in the usa
David E Newman-Toker, Najlla Nassery, Adam C Schaffer, Chihwen Winnie Yu-Moe, Gwendolyn D Clemens, Zheyu Wang, Yuxin Zhu, Ali S. Saber Tehrani, Mehdi Fanai, Ahmed Hassoon, and Dana Siegal. 2023 · 2023
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Transformehr: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records
Zhichao Yang, Avijit Mitra, Weisong Liu, Dan Berlowitz, and Hong Yu. 2023 · 2023
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