Zobrazeno 1 - 10 of 44 pro vyhledávání: '"Dennis Shung"'
1
Akademický článek
Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review
Autor: Thomas Y T Lam, Max F K Cheung, Yasmin L Munro, Kong Meng Lim, Dennis Shung, Joseph J Y Sung
Publikováno v: Journal of Medical Internet Research, Vol 24, Iss 8, p e37188 (2022)
BackgroundThe number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of the clinical benefits of implementing AI-assisted tools in patient care. ObjectiveThis study
Externí odkaz: https://doaj.org/article/bc9bbd8c44cb4bc9b82453d10f9b9c9e
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2
Akademický článek
Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
Autor: Dennis Shung, Jessie Huang, Egbert Castro, J. Kenneth Tay, Michael Simonov, Loren Laine, Ramesh Batra, Smita Krishnaswamy
Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may im
Externí odkaz: https://doaj.org/article/70ecc46b9e1640a0ba5d4390d8202c6e
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3
Akademický článek
Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference
Autor: Matthew Amodio, Dennis Shung, Daniel B. Burkhardt, Patrick Wong, Michael Simonov, Yu Yamamoto, David van Dijk, Francis Perry Wilson, Akiko Iwasaki, Smita Krishnaswamy
Publikováno v: Patterns, Vol 2, Iss 7, Pp 100288- (2021)
Summary: Often when biological entities are measured in multiple ways, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other informati
Externí odkaz: https://doaj.org/article/e0eead5ed2a3472898bb3458cbcb81bf
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5
Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
Autor: Jessie Huang, Ramesh Batra, Egbert Castro, Michael Simonov, J. Kenneth Tay, Dennis Shung, Smita Krishnaswamy, Loren Laine
Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Scientific Reports
Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve res
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8703186ab7d1290e6732d4c4172d0f37
https://doaj.org/article/70ecc46b9e1640a0ba5d4390d8202c6e
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6
Advancing care for acute gastrointestinal bleeding using artificial intelligence
Autor: Dennis Shung
Publikováno v: Journal of Gastroenterology and Hepatology. 36:273-278
The future of gastrointestinal bleeding will include the integration of machine learning algorithms to enhance clinician risk assessment and decision making. Machine learning algorithms have shown promise in outperforming existing clinical risk score
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8476c409b6d755a2cb0c0372d90e80bc
https://doi.org/10.1111/jgh.15372
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7
Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules
Autor: Loren Laine, Michael Simonov, Caitlin Partridge, David Chang, Dennis Shung, Fan Li, Andrew Taylor, Cynthia Tsay, J. Kenneth Tay, Prem Thomas, Allen L. Hsiao
Publikováno v: Journal of Gastroenterology and Hepatology. 36:1590-1597
BACKGROUND AND AIM Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d88e04e3ebceb14d3cce28ce48882625
https://doi.org/10.1111/jgh.15313
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10
EMBEDDING SIGNALS ON GRAPHS WITH UNBALANCED DIFFUSION EARTH MOVER’S DISTANCE
Autor: Alexander Tong, Guillaume Huguet, Dennis Shung, Amine Natik, Manik Kuchroo, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy
Publikováno v: Proc IEEE Int Conf Acoust Speech Signal Process
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observations in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a46e2a788c9e0b20bf4b172c20ac3710
https://europepmc.org/articles/PMC9828741/
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