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Artificial Intelligence for Healthcare: Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
by Sze-chuan Suen (Editor), David Scheinker (Editor), Eva Enns (Editor)
English | 350 pages | Cambridge University Press; New edition (May 5, 2022) | 1108836739 | PDF | 9.07 Mb

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.

Code:

https://k2s.cc/file/fd2bf32acddc8/1108836739.pdf

Code:

https://rapidgator.net/file/bbf665c8765e5b00650107d2bc1459c1/1108836739.pdf.html