Artificial Intelligence for Healthcare: Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World

doi:10.1017/9781108872188

Artificial Intelligence for Healthcare

Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World

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Khan, Adan Omokhoa, Venus Oseyi and Afzal, Safa Inaya 2024. Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems. p. 1.

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Edited by Sze-chuan Suen, University of Southern California, David Scheinker, Stanford University, California, Eva Enns, University of Minnesota

Publisher:: Cambridge University Press
Online publication date:: April 2022
Print publication year:: 2022
Online ISBN:: 9781108872188
DOI:: https://doi.org/10.1017/9781108872188

Subjects:: Engineering, Knowledge Management, Databases and Data Mining, Industrial Manufacturing and Operations Engineering, Computer Science

69.99 (GBP)

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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.

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Artificial Intelligence for Healthcare

Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World

This Book has been cited by the following publications. This list is generated based on data provided by Crossref.

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Contents

Artificial Intelligence for Healthcare
pp i-ii

Artificial Intelligence for Healthcare - Title page
pp iii-iii

Copyright page
pp iv-iv

Contents
pp v-vi

Contributors
pp vii-viii

Preface
pp ix-x

Introduction
pp 1-12

1 - Artificial Intelligence and Public Health
pp 3-12

Part I - Personalized Medicine
pp 13-80

2 - How AI Can Help Depression Treatment
pp 15-36

3 - Personalizing Medicine
pp 37-59

4 - Proceed with Care
pp 60-80

Part II - Optimizing Healthcare Systems
pp 81-82

5 - Using Algorithmic Solutions to Address Gatekeeper Training Issues for Suicide Prevention on College Campuses
pp 83-109

6 - Optimizing Defibrillator Deployment
pp 110-140

7 - Optimization of Biomarker-Based Prostate Cancer Screening Policies
pp 141-158

8 - Analytics-Driven Capacity Management
pp 159-181

9 - Practical Advice for Clinician–Engineer Partnerships for the Use of AI, Optimization, and Analytics for Healthcare Delivery
pp 182-192

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Artificial Intelligence for Healthcare

Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World

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