Decision Analytics and Optimization in Disease Prevention and Treatment

Herausgeber: Kong, Nan; Zhang, Shengfan

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Wiley
• Seitenzahl: 432
• Erscheinungstermin: 13. März 2018
• Englisch
• Abmessung: 231mm x 155mm x 28mm
• Gewicht: 771g
• ISBN-13: 9781118960127
• ISBN-10: 1118960122
• Artikelnr.: 42054592

Autorenporträt

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.

Inhaltsangabe

CONTRIBUTORS xiii
PREFACE xvii
PART 1 INFECTIOUS DISEASE CONTROL AND MANAGEMENT 1
1 Optimization in Infectious Disease Control and Prevention: Tuberculosis
Modeling Using Microsimulation 3
Sze?-chuan Suen
1.1 Tuberculosis Epidemiology and Background 4
1.1.1 TB in India 5
1.2 Microsimulations for Disease Control 6
1.3 A Microsimulation for Tuberculosis Control in India 8
1.3.1 Population Dynamics 9
1.3.2 Dynamics of TB in India 9
1.3.3 Activation 10
1.3.4 TB Treatment 11
1.3.5 Probability Conversions 13
1.3.6 Calibration and Validation 14
1.3.7 Intervention Policies and Analysis 16
1.3.8 Time Horizons and Discounting 18
1.3.9 Incremental Cost?-Effectiveness Ratios and Net Monetary Benefits 19
1.3.10 Sensitivity Analysis 22
1.4 Conclusion 22
References 23
2 Saving Lives with Operations Research: Models to Improve HIV Resource
Allocation 25
Sabina S. Alistar and Margaret L. Brandeau
2.1 Introduction 25
2.1.1 Background 25
2.1.2 Modeling Approaches 27
2.1.3 Chapter Overview 31
2.2 HIV Resource Allocation: Theoretical Analyses 31
2.2.1 Defining the Resource Allocation Problem 31
2.2.2 Production Functions for Prevention and Treatment Programs 35
2.2.3 Allocating Resources among Prevention and Treatment Programs 37
2.3 HIV Resource Allocation: Portfolio Analyses 39
2.3.1 Portfolio Analysis 39
2.3.2 O piate Substitution Therapy and ART in Ukraine 40
2.3.3 Pre?-exposure Prophylaxis and ART 42
2.4 HIV Resource Allocation: A Tool for Decision Makers 44
2.4.1 REACH Model Overview 44
2.4.2 Example Analysis: Brazil 45
2.4.3 Example Analysis: Thailand 48
2.5 Discussion and Further Research 50
Acknowledgment 53
References 53
3 Adaptive Decision?-Making During Epidemics 59
Reza Yaesoubi and Ted Cohen
3.1 Introduction 59
3.2 Problem Formulation 61
3.3 Methods 63
3.3.1 The 1918 Influenza Pandemic in San Francisco, CA 63
3.3.2 Stochastic Transmission Dynamic Models 64
3.3.3 Calibration 66
3.3.4 O ptimizing Dynamic Health Policies 69
3.4 Numerical Results 73
3.5 Conclusion 75
Acknowledgments 76
References 76
4 Assessing Register?-Based Chlamydia Infection Screening Strategies: A
Cost?-Effectiveness Analysis on Screening Start/End Age and Frequency 81
Yu Teng, Nan Kong, and Wanzhu Tu
4.1 Introduction 81
4.2 Background Literature Review 83
4.2.1 Clinical Background on CT Infection and Control 83
4.2.2 CT Screening Programs 85
4.2.3 Computational Modeling on CT Transmission and Control 85
4.3 Mathematical Modeling 89
4.3.1 An Age?-Structured Compartmental Model 89
4.3.2 Model Parameterization and Validation 93
4.4 Strategy Assessment 98
4.4.1 Base?-Case Assessment 98
4.4.2 Sensitivity Analysis 100
4.5 Conclusions and Future Research 101
References 102
5 Optimal Selection of Assays for Detecting Infectious Agents in Donated
Blood 109
Ebru K. Bish, Hadi El?-Amine, Douglas R. Bish, Susan L. Stramer, and
Anthony D. Slonim
5.1 Introduction and Challenges 109
5.1.1 Introduction 109
5.1.2 The Challenges 111
5.2 The Notation and Decision Problem 113
5.2.1 Notation 114
5.2.2 Measures of Interest 115
5.2.3 Model Formulation 117
5.2.4 Relationship of the Proposed Mathematical Models to
Cost?-Effectiveness Analysis 118
5.3 The Case Study of the Sub?-Saharan Africa Region and the United States
119
5.3.1 Uncertainty in Prevalence Rates 122
5.4 Contributions and Future Research Directions 123
Acknowledgments 123
References 124
6 Modeling Chronic Hepatitis C During Rapid Therapeutic Advance:
Cost?-Effective Screening, Monitoring, and Treatment Strategies 129
Shan Liu
6.1 Introduction 129
6.2 Method 131
6.2.1 Modeling Disease Natural History and Intervention 132
6.2.2 Estimating Parameters for Disease Progression and Death 134
6.3 Four Research Areas in Designing Effective HCV Interventions 139
6.3.1 Cost?-Effective Screening and Treatment Strategies 139
6.3.2 Cost?-Effective Monitoring Guidelines 141
6.3.3 O ptimal Treatment Adoption Decisions 141
6.3.4 O ptimal Treatment Delivery in Integrated Healthcare Systems 145
6.4 Concluding Remarks 148
References 148
PART 2 NONCOMMUNICABLE DISEASE PREVENTION 153
7 Modeling Disease Progression and Risk?-Differentiated Screening for
Cervical Cancer Prevention 155
Adriana Ley?-Chavez and Julia L. Higle
7.1 Introduction 155
7.2 Literature Review 157
7.3 Modeling Cervical Cancer Screening 159
7.3.1 Model Components 160
7.3.2 Parameter Selection 166
7.3.3 Implementation 169
7.4 Model?-Based Analyses 171
7.4.1 Cost?-Effectiveness Analysis 171
7.4.2 Sensitivity Analysis 172
7.5 Concluding Remarks 174
References 175
8 Using Finite?-Horizon Markov Decision Processes for Optimizing
Post?-Mammography Diagnostic Decisions 183
Sait Tunc, Oguzhan Alagoz, Jagpreet Chhatwal, and Elizabeth S. Burnside
8.1 Introduction 183
8.2 Model Formulations 185
8.3 Structural Properties 188
8.4 Numerical Results 193
8.5 Summary 196
Acknowledgments 196
References 197
9 Partially Observable Markov Decision Processes for Prostate Cancer
Screening, Surveillance, and Treatment: A Budgeted Sampling Approximation
Method 201
Jingyu Zhang and Brian T. Denton
9.1 Introduction 201
9.2 Review of POMDP Models and Benchmark Algorithms 204
9.3 A POMDP Model for Prostate Cancer Screening, Surveillance, and
Treatment 206
9.4 Budgeted Sampling Approximation 209
9.4.1 Lower and Upper Bounds 209
9.4.2 Summary of the Algorithm 211
9.5 Computational Experiments 213
9.5.1 Finite?-Horizon Test Instances 213
9.5.2 Computational Experiments 214
9.6 Conclusions 217
References 219
10 Cost?-Effectiveness Analysis of Breast Cancer Mammography Screening
Policies Considering Uncertainty in Women's Adherence 223
Mahboubeh Madadi and Shengfan Zhang
10.1 Introduction 223
10.2 Model Formulation 225
10.3 Numerical Studies 231
10.4 Results 233
10.4.1 Perfect Adherence Case 233
10.4.2 General Population Adherence Case 234
10.5 Summary 236
References 237
11 An Agent?-Based Model for Ideal Cardiovascular Health 241
Yan Li, Nan Kong, Mark A. Lawley, and José A. Pagán
11.1 Introduction 241
11.2 Methodology 243
11.2.1 Agent?-Based Modeling 243
11.2.2 Model Structure 244
11.2.3 Parameter Estimation 246
11.2.4 User Interface 248
11.2.5 Model Validation 249
11.3 Results 250
11.3.1 Simulating American Adults 250
11.4 Simulating the Medicare?-Age Population and the Disease?-Specific
Subpopulations 252
11.5 Future Research 254
11.6 Summary 255
References 255
PART 3 TREATMENT TECHNOLOGY AND SYSTEM 259
12 Biological Planning Optimization for High?-Dose?-Rate Brachytherapy and
its Application to Cervical Cancer Treatment 261
Eva K. Lee, Fan Yuan, Alistair Templeton, Rui Yao, Krystyna Kiel, and James
C.H. Chu
12.1 Introduction 261
12.2 Challenges and Objectives 263
12.3 Materials and Methods 265
12.3.1 High?-Dose?-Rate Brachytherapy 265
12.3.2 PET Image 266
12.3.3 Novel OR?-Based Treatment?-Planning Model 266
12.3.4 Computational Challenges and Solution Strategies 271
12.4 Validation and Results 273
12.5 Findings, Implementation, and Challenges 276
12.6 Impact and Significance 279
12.6.1 Quality of Care and Quality of Life for Patients 279
12.6.2 Advancing the Cancer Treatment Frontier 279
12.6.3 Advances in Operations Research Methodologies 280
Acknowledgment 281
References 281
13 Fluence Map Optimization in Intensity?-Modulated Radiation Therapy
Treatment Planning 285
Dionne M. Aleman
13.1 Introduction 285
13.2 Treatment Plan Evaluation 288
13.2.1 Physical Dose Measures 289
13.2.2 Biological Dose Measures 291
13.3 FMO Optimization Models 292
13.3.1 O bjective Functions 293
13.3.2 Constraints 295
13.3.3 Robust Formulation 297
13.4 O ptimization Approaches 299
13.5 Conclusions 300
References 301
14 Sliding Window IMRT and VMAT Optimization 307
David Craft and Tarek Halabi
14.1 Introduction 307
14.2 Two?-Step IMRT Planning 309
14.3 O ne?-Step IMRT Planning 310
14.3.1 O ne?-Step Sliding Window Optimization 310
14.4 Volumetric Modulated ARC Therapy 313
14.5 Future Work for Radiotherapy Optimization 315
14.5.1 Custom Solver for Radiotherapy 315
14.5.2 Incorporating Additional Hardware Considerations into Sliding Window
VMAT Planning 315
14.5.3 Trade?-Off between Delivery Time and Plan Quality 316
14.5.4 What Do We Optimize? 316
14.6 Concluding Thoughts 317
References 318
15 Modeling the Cardiovascular Disease Prevention-Treatment Trade?-Off 323
George Miller
15.1 Introduction 323
15.2 Methods 325
15.2.1 Model Overview 325
15.2.2 Model Structure 327
15.2.3 Model Inputs 331
15.3 Results 334
15.3.1 Base Case 334
15.3.2 Interaction between Prevention and Treatment Spending 335
15.3.3 Impact of Discount Rate on Cost?-Effectiveness 336
15.3.4 O ptimal Spending Mix 337
15.3.5 Impact of Prevention Lag on Optimal Mix 338
15.3.6 Impact of Discount Rate on Optimal Mix 340
15.3.7 Impact of Time Horizon on Optimal Mix 340
15.3.8 Impacts of Research 341
15.4 Discussion 344
Acknowledgment 346
References 346
16 Treatment Optimization for Patients with Type 2 Diabetes 349
Jennifer Mason Lobo
16.1 Introduction 349
16.2 Literature Review 350
16.3 Model Formulation 353
16.3.1 Decision Epochs 354
16.3.2 States 354
16.3.3 Actions 355
16.3.4 Probabilities 355
16.3.5 Rewards 356
16.3.6 Value Function 356
16.4 Numerical Results 357
16.4.1 Model Inputs 357
16.4.2 Optimal Treatment Policies to Reduce Polypharmacy 358
16.5 Conclusions 362
References 363
17 Machine Learning for Early Detection and Treatment Outcome Prediction
367
Eva K. Lee
17.1 Introduction 367
17.2 Background 369
17.3 Machine Learning with Discrete Support Vector Machine Predictive
Models 372
17.3.1 Modeling of Reserved?-Judgment Region for General Groups 373
17.3.2 Discriminant Analysis via Mixed?-Integer Programming 374
17.3.3 Model Variations 376
17.3.4 Theoretical Properties and Computational Strategies 379
17.4 Applying Damip to Real?-World Applications 380
17.4.1 Validation of Model and Computational Effort 381
17.4.2 Applications to Biological and Medical Problems 381
17.4.3 Applying DAMIP to UCI Repository of Machine Learning Databases 389
17.5 Summary and Conclusion 393
Acknowledgment 394
References 394
INDEX 401