PART 1 INFECTIOUS DISEASE CONTROL AND MANAGEMENT 1
1 Optimization in Infectious Disease Control and Prevention: Tuberculosis Modeling Using Microsimulation 3
2 Saving Lives with Operations Research: Models to Improve HIV Resource Allocation 25
3 Adaptive Decision-Making During Epidemics 59
4 Assessing Register-Based Chlamydia Infection Screening Strategies: A Cost-Effectiveness Analysis on Screening Start/End Age and Frequency 81
5 Optimal Selection of Assays for Detecting Infectious Agents in Donated Blood 109
6 Modeling Chronic Hepatitis C During Rapid Therapeutic Advance: Cost-Effective Screening, Monitoring, and Treatment Strategies 129
PART 2 NONCOMMUNICABLE DISEASE PREVENTION 153
7 Modeling Disease Progression and Risk-Differentiated Screening for Cervical Cancer Prevention 155
8 Using Finite-Horizon Markov Decision Processes for Optimizing Post-Mammography Diagnostic Decisions 183
9 Partially Observable Markov Decision Processes for Prostate Cancer Screening, Surveillance, and Treatment: A Budgeted Sampling Approximation Method 201
10 Cost-Effectiveness Analysis of Breast Cancer Mammography Screening Policies Considering Uncertainty in Womens Adherence 223
11 An Agent-Based Model for Ideal Cardiovascular Health 241
PART 3 TREATMENT TECHNOLOGY AND SYSTEM 259
12 Biological Planning Optimization for High-Dose-Rate Brachytherapy and its Application to Cervical Cancer Treatment 261
13 Fluence Map Optimization in Intensity-Modulated Radiation Therapy Treatment Planning 285
14 Sliding Window IMRT and VMAT Optimization 307
15 Modeling the Cardiovascular Disease PreventionTreatment Trade-Off 323
16 Treatment Optimization for Patients with Type 2 Diabetes 349
17 Machine Learning for Early Detection and Treatment Outcome Prediction 367
A systematic review of the most current decision models and techniques for disease prevention and treatment
Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource of the most current decision models and techniques for disease prevention and treatment. With contributions from leading experts in the field, this important resource presents information on the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology. Designed to be accessible, in each chapter the text presents one decision problem with the related methodology to showcase the vast applicability of operations research tools and techniques in advancing medical decision making.
This vital resource features the most recent and effective approaches to the quickly growing field of healthcare decision analytics, which involves cost-effectiveness analysis, stochastic modeling, and computer simulation. Throughout the book, the contributors discuss clinical applications of modeling and optimization techniques to assist medical decision making within complex environments. Accessible and authoritative, Decision Analytics and Optimization in Disease Prevention and Treatment:
Presents summaries of the state-of-the-art research that has successfully utilized both decision analytics and optimization tools within healthcare operations research
Highlights the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology
Includes contributions by well-known experts from operations researchers to clinical researchers, and from data scientists to public health administrators
Offers clarification on common misunderstandings and misnomers while shedding light on new approaches in this growing area
Designed for use by academics, practitioners, and researchers, Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource for accessing the power of decision analytics and optimization tools within healthcare operations research.
NAN KONG, PhD, is Associate Professor in the Weldon School of Biomedical Engineering at Purdue University. Dr. Kong is a member of INFORMS and SMDM, and his research interests include healthcare resource allocation, medical decision-making, and hospital operations management.
SHENGFAN ZHANG, PhD, is Assistant Professor in the Department of Industrial Engineering at the University of Arkansas. Dr. Zhang is a member of INFORMS and IISE, and her research interests include mathematical modeling of stochastic systems, medical decision-making, and health analytics.