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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2022
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245 | 0 | 0 | |a Artificial intelligence for healthcare |b interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world |c edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, Eva Enns, University of Minnesota |
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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. | ||
700 | 1 | |a Enns, Eva |d 1984- | |
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700 | 1 | |a Suen, Sze-chuan |d 1987- | |
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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 Cambridge Cambridge University Press 2022 1 Online-Ressource (x, 192 Seiten) txt c cr 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- Erscheint auch als Druck-Ausgabe 9781108836739 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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