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pro vyhledávání: '"Hur, Kyunghoon"'
The increasing volume of electronic health records (EHRs) across healthcare institutions presents the opportunity to enhance model accuracy and robustness in clinical prediction tasks. Federated learning enables training on data from multiple institu
Externí odkaz:
http://arxiv.org/abs/2404.13318
Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain. Recent work presented a promising framework that embeds entire features in raw EHR data regardless of its for
Externí odkaz:
http://arxiv.org/abs/2303.08290
Autor:
Hur, Kyunghoon, Oh, Jungwoo, Kim, Junu, Kim, Jiyoun, Lee, Min Jae, Cho, Eunbyeol, Moon, Seong-Eun, Kim, Young-Hak, Choi, Edward
Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models. To address this challenge, we propose Universal Healthcare Predictive Framework (UniHPF), which r
Externí odkaz:
http://arxiv.org/abs/2211.08082
Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR). Despite its guarantee of privacy protection, the wide application of FL is restricted by two large challenges: the heterogeneous EHR s
Externí odkaz:
http://arxiv.org/abs/2211.07300
Autor:
Hur, Kyunghoon, Oh, Jungwoo, Kim, Junu, Kim, Jiyoun, Lee, Min Jae, Cho, Eunbyeol, Moon, Seong-Eun, Kim, Young-Hak, Atallah, Louis, Choi, Edward
Publikováno v:
IEEE Journal of Biomedical and Health Informatics 2024
Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a particular task, based on specific data formats available in a set of med
Externí odkaz:
http://arxiv.org/abs/2207.09858
EHR systems lack a unified code system forrepresenting medical concepts, which acts asa barrier for the deployment of deep learningmodels in large scale to multiple clinics and hos-pitals. To overcome this problem, we introduceDescription-based Embed
Externí odkaz:
http://arxiv.org/abs/2111.09098
Autor:
Ku, Yongsuk, Park, Joo-Hwan, Cho, Ryeongeun, Lee, Yongki, Park, Hyoung-Min, Kim, MinA, Hur, Kyunghoon, Byun, Soo Young, Liu, Jun, Lee, Young-suk, Shum, David, Shin, Dong-Yeop, Koh, Youngil, Cho, Je-Yoel, Yoon, Sung-Soo, Hong, Junshik, Kim, Yoosik
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Mar . 118(13), 1-12.
Externí odkaz:
https://www.jstor.org/stable/27039802
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Publikováno v:
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2023 Oct 27; Vol. PP. Date of Electronic Publication: 2023 Oct 27.