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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Artificial intelligence for healthcare :/ edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, Eva Enns, University of Minnesota.
Reminder of title:	interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world /
other author:	Suen, Sze-chuan,
Description:	1 online resource (x, 192 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 07 Apr 2022).
Subject:	Medical informatics. -
Online resource:	https://doi.org/10.1017/9781108872188
ISBN:	9781108872188 (ebook)

Artificial intelligence for healthcare : = interdisciplinary partnerships for analytics-driven improvements in a Post-COVID world /
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. - 1 online resource (x, 192 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 07 Apr 2022).

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.

ISBN: 9781108872188 (ebook)Subjects--Topical Terms:

583858
Medical informatics.

LC Class. No.: R858 / .A768 2022

Dewey Class. No.: 362.10285

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