医療のためのAI:パンデミック後の世界のデータ分析による改善のための学際的連携<br>Artificial Intelligence for Healthcare : Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World

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医療のためのAI:パンデミック後の世界のデータ分析による改善のための学際的連携
Artificial Intelligence for Healthcare : Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World

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  • 製本 Hardcover:ハードカバー版/ページ数 350 p.
  • 言語 ENG
  • 商品コード 9781108836739
  • DDC分類 610.28563

Full Description

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.

Contents

Introduction Sze-chuan Suen, Eva Enns and David Scheinker; 1. Artificial Intelligence and Public Health: Opportunities Abound Sheldon H. Jacobson and Janet A. Jokela; Part I. Personalized Medicine: 2. How AI Can Help Depression Care - Designing Patient-Specific Adaptive Monitoring Algorithms Shan Liu and Shuai Huang; 3. Personalizing Medicine -Estimating Heterogeneous Treatment Effects Tony Duan and Sanjay Basu; 4. Proceed with Care - Integrating Predictive Analytics with Patient Decision-Making Hamsa Bastani and Pengyi Shi; Part II. Optimizing Health Care Systems: 5. Using Algorithmic Solutions to Address Gatekeeper Training Issues on College Campuses Anthony Fulginiti, Aida Rahmattalabi, Jarrod Call, Phebe Vayanos, and Eric Rice; 6. Optimizing Defibrillator Deployment Timothy C.Y. Chan and Christopher L.F. Sun; 7. Optimization of Biomarker-Based Prostate Cancer Screening Policies Christine Barnett and Brian Denton; 8. Analytics-Driven Hospital Resource Management - Principles and Practical Lessons from Projects at Three Hospitals Margaret L. Brandeau and David Scheinker; 9. Practical advice for clinician-engineer partnerships for the use of AI, optimization, and analytics for healthcare delivery David Scheinker, Robert A. Harrington, and Fatima Rodriguez.

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