Artificial intelligence for healthcare interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world

Healthcare has recently seen numerous exciting applications of artificial intelligence, industrial engineering, and operations research. This book, designed to be accessible to a diverse audience, provides an overview of interdisciplinary research partnerships that leverage AI, IE, and OR to tackle...

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Weitere Verfasser: Enns, Eva 1984-, Scheinker, David, Suen, Sze-chuan 1987-
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Sprache:English
Veröffentlicht: Cambridge Cambridge University Press 2022
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520 |a Healthcare has recently seen numerous exciting applications of artificial intelligence, industrial engineering, and operations research. This book, designed to be accessible to a diverse audience, provides an overview of interdisciplinary research partnerships that leverage AI, IE, and OR to tackle societal and operational problems in healthcare. The topics are drawn from a wide variety of disciplines, ranging from optimizing the location of AEDs for cardiac arrests to data mining for facilitating patient flow through a hospital. These applications highlight how engineering has contributed to medical knowledge, health system operations, and behavioral health. Chapter authors include medical doctors, policy-makers, social scientists, and engineers. Each chapter begins with a summary of the health care problem and engineering method. In these examples, researchers in public health, medicine, and social science as well as engineers will find a path to start interdisciplinary collaborations in health applications of AI/IE/OR. 
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spelling Artificial intelligence for healthcare interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, Eva Enns, University of Minnesota
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1 Online-Ressource (x, 192 Seiten)
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Healthcare has recently seen numerous exciting applications of artificial intelligence, industrial engineering, and operations research. This book, designed to be accessible to a diverse audience, provides an overview of interdisciplinary research partnerships that leverage AI, IE, and OR to tackle societal and operational problems in healthcare. The topics are drawn from a wide variety of disciplines, ranging from optimizing the location of AEDs for cardiac arrests to data mining for facilitating patient flow through a hospital. These applications highlight how engineering has contributed to medical knowledge, health system operations, and behavioral health. Chapter authors include medical doctors, policy-makers, social scientists, and engineers. Each chapter begins with a summary of the health care problem and engineering method. In these examples, researchers in public health, medicine, and social science as well as engineers will find a path to start interdisciplinary collaborations in health applications of AI/IE/OR.
Enns, Eva 1984-
Scheinker, David
Suen, Sze-chuan 1987-
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TUM01 ZDB-20-CTM TUM_PDA_CTM https://doi.org/10.1017/9781108872188 Volltext
spellingShingle Artificial intelligence for healthcare interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world
title Artificial intelligence for healthcare interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world
title_auth Artificial intelligence for healthcare interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world
title_exact_search Artificial intelligence for healthcare interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world
title_full Artificial intelligence for healthcare interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, Eva Enns, University of Minnesota
title_fullStr Artificial intelligence for healthcare interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, Eva Enns, University of Minnesota
title_full_unstemmed Artificial intelligence for healthcare interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, Eva Enns, University of Minnesota
title_short Artificial intelligence for healthcare
title_sort artificial intelligence for healthcare interdisciplinary partnerships for analytics driven improvements in a post covid world
title_sub interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world
url https://doi.org/10.1017/9781108872188
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