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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| Other Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2022
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| Subjects: | |
| Online Access: | BSB01 BTU01 FHN01 FWS01 FWS02 TUM01 URL des Erstveröffentlichers |
| Summary: | 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. |
| Item Description: | Title from publisher's bibliographic system (viewed on 07 Apr 2022) |
| Physical Description: | 1 Online-Ressource (x, 192 Seiten) |
| ISBN: | 9781108872188 |
| DOI: | 10.1017/9781108872188 |
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| isbn | 9781108872188 |
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| spellingShingle | Artificial intelligence for healthcare interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world Medical informatics Medical care / Data processing Artificial intelligence / Medical applications |
| 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_exact_search_txtP | 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 |
| topic | Medical informatics Medical care / Data processing Artificial intelligence / Medical applications |
| topic_facet | Medical informatics Medical care / Data processing Artificial intelligence / Medical applications |
| url | https://doi.org/10.1017/9781108872188 |
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