LEADER 01982cam a2200229 a 450
001 1164680
008 230926s2022 enka e b 000 0 eng
010 $a 2021054362
020 $a9781108836739$q(hbk.) :$cNT$2695
035 $aNO000235156
037 $b公共圖書館臺南分區資源中心
040 $aDLC$beng$cDLC$dTWTNM
050 00$aR858$bA768 2022
082 0 $a362.10285$223
090 $a臺南市立圖書館
245 00$aArtificial intelligence for healthcare :$binterdisciplinary partnerships for analytics-driven improvements in a Post-COVID world /$cedited by Sze-chuan Suen, David Scheinker, Eva Enns.
246 30$aInterdisciplinary partnerships for analytics-driven improvements in a Post-COVID world
260 $aCambridge, United Kingdom ;$aNew York, NY :$bCambridge University Press,$c2022.
300 $ax, 192 p. :$bill. ;$c24 cm.
504 $aIncludes bibliographical references.
520 $a"Healthcare has recently seen numerous exciting applications of artificial intelligence, industrial engineering, and operations research. This book, designed to be accessible to a diverse audience, provides an overview of interdisciplinary research partnerships that leverage AI, IE, and OR to tackle societal and operational problems in healthcare. The topics are drawn from a wide variety of disciplines, ranging from optimizing the location of AEDs for cardiac arrests to data mining for facilitating patient flow through a hospital. These applications highlight how engineering has contributed to medical knowledge, health system operations, and behavioral health. Chapter authors include medical doctors, policy-makers, social scientists, and engineers. Each chapter begins with a summary of the health care problem and engineering method. In these examples, researchers in public health, medicine, and social science as well as engineers will find a path to start interdisciplinary collaborations in health applications of AI/IE/OR"--$cProvided by publisher.
650 0$aMedical informatics.
650 0$aMedical care$xData processing.
650 0$aArtificial intelligence$xMedical applications.
653 $a科技創新
653 $a知識性
700 1 $aSuen, Sze-chuan,$d1987-
700 1 $aScheinker, David.
700 1 $aEnns, Eva,$d1984-