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Suen / Scheinker / Enns

Artificial Intelligence for Healthcare

Medium: Buch
ISBN: 978-1-108-83673-9
Verlag: Cambridge University Press
Erscheinungstermin: 05.04.2022
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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.

Produkteigenschaften

  • Artikelnummer: 9781108836739
  • Medium: Buch
  • ISBN: 978-1-108-83673-9
  • Verlag: Cambridge University Press
  • Erscheinungstermin: 05.04.2022
  • Sprache(n): Englisch
  • Auflage: Erscheinungsjahr 2022
  • Produktform: Gebunden
  • Gewicht: 454 g
  • Seiten: 350
  • Format (B x H x T): 157 x 235 x 16 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

Sze-chuan Suen is an assistant professor in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California. She received her PhD in the department of Management Science and Engineering from Stanford University in 2016. Her research interests include developing applied mathematical models to identify epidemiological trends and evaluating health policies to support informed decision-making. Her work in health policy modeling draws from a variety of techniques, including simulation, dynamic systems modeling, Markov decision processes, cost-effectiveness analysis, and decision analysis. Her previous work has examined the optimal management of tuberculosis, HIV, and chronic diseases.

David Scheinker is a Clinical Associate Professor of Pediatrics in the Stanford School of Medicine and the Executive Director of Systems Design and Collaborative Research at the Stanford Lucile Packard Children's Hospital. He is the Founder and Director of SURF Stanford Medicine (surf.stanford.edu), a group that brings together students and faculty from the university with physicians, nurses, and administrators from the hospitals to improve the quality of care using operations research methodology. His research focuses on applications of operations research in healthcare. Previously, he was a Joint Research Fellow at The MIT Sloan School of Management and Massachusetts General Hospital.

Eva Enns is an Associate Professor in the Division of Health Policy and Management at the University of Minnesota School of Public Health. She received her PhD in Electrical Engineering from Stanford University in 2012 and her dissertation was awarded the Decision Sciences Institute Elwood S. Buffa Doctoral Dissertation Award in 2013. In her research, she applies engineering concepts, including simulation modelling, optimization, cost-effectiveness analysis, and resource allocation, to help inform policies for the prevention and treatment of infectious diseases. Specific application areas include HIV, sexually transmitted infections, antimicrobial resistance, and most recently COVID-19.

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.