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

Overviews of interdisciplinary research partnerships applying AI, IE, and OR to societal and operational problems in healthcare settings.

Sze-chuan Suen (Edited by), David Scheinker (Edited by), Eva Enns (Edited by)

9781108836739, Cambridge University Press

Hardback, published 5 May 2022

350 pages
23.5 x 15.7 x 1.7 cm, 0.437 kg

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.

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.

Subject Areas: Data mining [UNF], Operating systems [UL], Ethical & social aspects of IT [UBJ], Health & safety aspects of IT [UBH], Epidemiology & medical statistics [MBNS], Public health & preventive medicine [MBN], Operational research [KJT]