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pro vyhledávání: '"Park, Briton"'
Autor:
Hsu, Aliyah R., Cherapanamjeri, Yeshwanth, Park, Briton, Naumann, Tristan, Odisho, Anobel Y., Yu, Bin
Pre-trained transformers are often fine-tuned to aid clinical decision-making using limited clinical notes. Model interpretability is crucial, especially in high-stakes domains like medicine, to establish trust and ensure safety, which requires human
Externí odkaz:
http://arxiv.org/abs/2305.17588
Precision medicine has the potential to revolutionize healthcare, but much of the data for patients is locked away in unstructured free-text, limiting research and delivery of effective personalized treatments. Generating large annotated datasets for
Externí odkaz:
http://arxiv.org/abs/2012.08113
Autor:
Dwivedi, Raaz, Tan, Yan Shuo, Park, Briton, Wei, Mian, Horgan, Kevin, Madigan, David, Yu, Bin
Building on Yu and Kumbier's PCS framework and for randomized experiments, we introduce a novel methodology for Stable Discovery of Interpretable Subgroups via Calibration (StaDISC), with large heterogeneous treatment effects. StaDISC was developed d
Externí odkaz:
http://arxiv.org/abs/2008.10109
Autor:
Altieri, Nick, Barter, Rebecca L., Duncan, James, Dwivedi, Raaz, Kumbier, Karl, Li, Xiao, Netzorg, Robert, Park, Briton, Singh, Chandan, Tan, Yan Shuo, Tang, Tiffany, Wang, Yu, Zhang, Chao, Yu, Bin
Publikováno v:
Published in Harvard Data Science Review, 2020
As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curation of a large data repository containing COVID-19 information from a ra
Externí odkaz:
http://arxiv.org/abs/2005.07882
Autor:
Lo, Derek, Park, Briton
Zika virus (ZIKV), a disease spread primarily through the Aedes aegypti mosquito, was identified in Brazil in 2015 and was declared a global health emergency by the World Health Organization (WHO). Epidemiologists often use common state-level attribu
Externí odkaz:
http://arxiv.org/abs/1612.03554
Publikováno v:
In Journal of Biomedical Informatics October 2021 122
Autor:
Hsu, Aliyah R., Cherapanamjeri, Yeshwanth, Park, Briton, Naumann, Tristan, Odisho, Anobel Y., Yu, Bin
Pre-trained transformer models have demonstrated success across many natural language processing (NLP) tasks. In applying these models to the clinical domain, a prevailing assumption is that pre-training language models from scratch on large-scale bi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68f414007aaf557e98e353712a98e552
Autor:
Lo, Derek1,2 derektlo@gmail.com, Park, Briton1,3
Publikováno v:
PLoS ONE. 2/13/2018, Vol. 13 Issue 2, p1-7. 7p.
Publikováno v:
JAMIA Open; Jul2021, Vol. 4 Issue 3, p1-8, 8p
Akademický článek
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