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Edited By
NAN KONG
Associate Professor
Weldon School of Biomedical Engineering, Purdue University
Regenstrief Center for Healthcare Engineering, Purdue University
West Lafayette, IN, USA
SHENGFAN ZHANG
Assistant Professor
Department of Industrial Engineering, University of Arkansas
Center for Innovation in Healthcare Logistics, University of Arkansas
Fayetteville, AR, USA
This edition first published 2018
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The right of Nan Kong, Shengfan Zhang to be identified as the Editor’s of this work has been asserted in accordance with law.
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Library of Congress Cataloging‐in‐Publication Data
Names: Kong, Nan, editor. | Zhang, Shengfan, editor.
Title: Decision analytics and optimization in disease prevention and treatment / edited by Nan Kong, Shengfan Zhang.
Description: Hoboken, NJ : Wiley, 2018. | Includes bibliographical references and index. |
Identifiers: LCCN 2017037696 (print) | LCCN 2017038531 (ebook) | ISBN 9781118960134 (pdf) | ISBN 9781118960141 (epub) | ISBN 9781118960127 (cloth)
Subjects: | MESH: Preventive Health Services | Communicable Disease Control | Decision Making | Models, Theoretical | Therapeutics
Classification: LCC RA643 (ebook) | LCC RA643 (print) | NLM WA 108 | DDC 616.9–dc23
LC record available at https://lccn.loc.gov/2017037696
Cover design by Wiley
Cover image: (Top right) © deepblue4you/Gettyimages; (Bottom left) © Skomorokh/Gettyimages
Oguzhan Alagoz, Department of Industrial and Systems Engineering, University of Wisconsin‐Madison, Madison, WI, USA
Dionne M. Aleman, Department of Mechanical & Industrial Engineering and Institute for Health Policy, Management & Evaluation, University of Toronto, Toronto, ON, Canada; Guided Therapeutics Core, Techna Institute, Toronto, ON, Canada
Sabina S. Alistar, Department of Management Science and Engineering, Stanford University, Palo Alto, CA, USA
Douglas R. Bish, Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA
Ebru K. Bish, Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA
Margaret L. Brandeau, Department of Management Science and Engineering, Stanford University, Palo Alto, CA, USA
Elizabeth S. Burnside, Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
Jagpreet Chhatwal, Institute of Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
James C.H. Chu, Department of Radiation Oncology, Rush University Medical Center, Chicago, IL, USA
Ted Cohen, Department of Epidemiology and Microbial Disease, Yale School of Public Health, New Haven, CT, USA
David Craft, Radiation Oncology, Department of Physics, Harvard Medical School, Boston, MA, USA
Brian T. Denton, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA
Hadi El‐Amine, Systems Engineering and Operations Research Department, George Mason University, Fairfax, VA, USA
Tarek Halabi, Radiation Oncology, Department of Physics, Harvard Medical School, Boston, MA, USA
Julia L. Higle, Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA
Krystyna Kiel, Department of Radiation Oncology, Rush University Medical Center, Chicago, IL, USA
Nan Kong, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
Mark A. Lawley, Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA; Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
Eva K. Lee, NSF‐Whitaker Center for Operations Research in Medicine and HealthCare, Georgia Institute of Technology, Atlanta, GA, USA; School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA; NSF I/UCRC Center for Health Organization Transformation, Arlington, VA, USA
Adriana Ley‐Chavez, Department of Industrial and Mechanical Engineering, Universidad de las Américas Puebla, Puebla, Mexico
Yan Li, Center for Health Innovation, The New York Academy of Medicine, New York, NY, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Shan Liu, Department of Industrial and Systems Engineering, University of Washington, Seattle, WA, USA
Jennifer Mason Lobo, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
Mahboubeh Madadi, Department of Industrial Engineering, Louisiana Tech University, Ruston, LA, USA
George Miller, Center for Value in Health Care, Altarum, Ann Arbor, MI, USA
José A. Pagán, Center for Health Innovation, The New York Academy of Medicine, New York, NY, USA; Department of Public Health Policy and Management, College of Global Public Health, New York University, New York, NY, USA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
Anthony D. Slonim, University of Nevada School of Medicine and Renown Health, Reno, NV, USA
Susan L. Stramer, Scientific Affairs, American Red Cross, Gaithersburg, MD, USA
Sze‐chuan Suen, Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA
Alistair Templeton, Department of Radiation Oncology, Rush University Medical Center, Chicago, IL, USA
Yu Teng, Avenir Health, Glastonbury, CT, USA
Wanzhu Tu, Department of Biostatistics, Indiana University Medical School, Indianapolis, IN, USA
Sait Tunc, Department of Industrial and Systems Engineering, University of Wisconsin‐Madison, Madison, WI, USA
Reza Yaesoubi, Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
Rui Yao, Department of Radiation Oncology, Rush University Medical Center, Chicago, IL, USA
Fan Yuan, NSF‐Whitaker Center for Operations Research in Medicine and HealthCare, Georgia Institute of Technology, Atlanta, GA, USA; School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA; NSF I/UCRC Center for Health Organization Transformation, Arlington, VA, USA
Shengfan Zhang, Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA
Jingyu Zhang, Enterprise Model Risk Management, Bank of America, Wilmington, DE, USA
Advances in disease prevention and treatment have greatly improved the quality of life of patients and the general population. However, it is challenging to truly harness these advances in patient‐centered medical decision‐making for the uncertainty associated with disease risks and care outcomes, as well as the complexity of the technologies. This book contains a collection of cutting‐edge research studies that apply decision analytics and optimization tools in disease prevention and treatment. Specifically, the book comprises the following three main parts.
Part 1: Infectious Disease Control and Management. Common infectious diseases are considered in this part, including tuberculosis (Chapter 1), HIV infection (Chapter 2), influenza (Chapter 3), chlamydia infection (Chapter 4), and hepatitis C (Chapter 6). Although not focusing on a specific type of infectious disease, Chapter 5 deals with the costs and efficacy of detecting infectious agents in donated blood. Controls and decisions investigated in this part include budget allocation (Chapter 2), school closure or children vaccination (Chapter 3), screening scheme design (Chapters 4 and 5), and a whole set of interventions (Chapter 6) such as behavior and public health interventions. Disease modeling techniques introduced in this part include microsimulation (Chapter 1), stochastic transmission dynamic model (Chapter 3), compartmental model (Chapter 4), and Markov‐based model (Chapter 6).
In this part, Chapters 1 and 6 provide excellent overviews of decision‐analytic modeling research in developing policy guidelines. Between the two chapters, the former focuses more on the disease modeling, whereas the latter focuses more on the analysis with a holistic view covering screening, monitoring, and treatment. In addition, Chapter 6 deals with long‐term management of an infectious disease, which helps make the transition to the second part of the book.
Part 2: Noncommunicable Disease Prevention. This part starts with Chapter 7, which examines screening strategies for the prevention of cervical cancers, which are mainly caused by human papillomavirus (HPV) infection. Chapter 7 concerns disease progression from the viewpoint of HPV infection rather than the infectious disease itself. The chapter provides a good connection with the first part of the book. Other prevalent noncommunicable diseases considered in this part include breast cancer (Chapters 8 and 10), prostate cancer (Chapter 9), and cardiovascular diseases (Chapter 11). Methodologies introduced in this part cover simulation with model‐based analyses for screening strategies (Chapter 7), Markov decision process (Chapter 8), partially observable Markov decision process (Chapter 9), cost‐effectiveness analysis under a partially observable Markov chain model (Chapter 10), and agent‐based modeling (Chapter 11).
Part 3: Treatment Technology and System. In this part, optimization studies of several treatment decisions and technologies are reported, including high‐dose‐rate brachytherapy (Chapter 12), intensity‐modulated radiation therapy (Chapters 13 and 14), volumetric modulated arc therapy (Chapter 14), cardiovascular disease prevention and treatment (Chapter 15), and various treatment decisions for type II diabetes (Chapter 16). Methodologies introduced comprise multiobjective, nonlinear, mixed‐integer programming model (Chapter 12), fluence map optimization (Chapter 13), sliding window optimization (Chapter 14), Markov modeling (Chapter 15), and Markov decision process (Chapter 16).
The book concludes with Chapter 17, which uniquely presents optimization‐based classification models for early detection of disease, risk prediction, and treatment design and outcome prediction. This chapter is expected to showcase extended potentials of optimization techniques and motivate more operations researchers to study biomedical data mining problems.
We believe this book can serve well as a handbook for researchers in the field of medical decision modeling, analysis, and optimization, a textbook for graduate‐level courses on OR applications in healthcare, and a reference for medical practitioners and public health policymakers with interest in health analytics.
Lastly, we would like to express our sincere gratitude to the following reviewers for taking their time to review book chapters and provide valuable feedback for our contributors in the blind‐review process: Turgay Ayer, Christine Barnett, Bjorn Berg, Margaret Brandeau, Brian Denton, Jeremy Goldhaber‐Fiebert, Shadi Hassani Goodarzi, Karen Hicklin, Julie Ivy, Amin Khademi, Anahita Khojandi, Yan Li, Jennifer Lobo, Maria Mayorga, Nisha Nataraj, Ehsan Salari, Burhan Sandikci, Joyatee Sarker, Carolina Vivas, Fan Wang, Xiaolei Xie, Yiwen Xu, and Yuanhui Zhang. We would also like to acknowledge the great support we received from Wiley editors, Sumathi Elangovan, Jon Gurstelle, Vishnu Narayanan, Kathleen Pagliaro, Vishnu Priya. R and former editor Susanne Steitz‐Filler.