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

ISBN-13: 9781108836739 / Angielski / Twarda / 2022 / 350 str.

Sze-chuan Suen (University of Southern California); California) David Scheinker (Stanford University;Eva Enns (University of Minnesota)

Opis

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.

Spis treści

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.