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Decision Analytics and Optimization in Disease Prevention and Treatment [Hardback]

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Decision Analytics and Optimization in Disease Prevention and TreatmentPalielināt
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Permanent link: https://www.kriso.lv/db/9781118960127.html
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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.
Contributors xiii
Preface xvii
Part 1 Infectious Disease Control And Management 1(152)
1 Optimization in Infectious Disease Control and Prevention: Tuberculosis Modeling Using Microsimulation
3(22)
Sze-chuan Suen
1.1 Tuberculosis Epidemiology and Background
4(2)
1.1.1 TB in India
5(1)
1.2 Microsimulations for Disease Control
6(2)
1.3 A Microsimulation for Tuberculosis Control in India
8(14)
1.3.1 Population Dynamics
9(1)
1.3.2 Dynamics of TB in India
9(1)
1.3.3 Activation
10(1)
1.3.4 TB Treatment
11(2)
1.3.5 Probability Conversions
13(1)
1.3.6 Calibration and Validation
14(2)
1.3.7 Intervention Policies and Analysis
16(2)
1.3.8 Time Horizons and Discounting
18(1)
1.3.9 Incremental Cost-Effectiveness Ratios and Net Monetary Benefits
19(3)
1.3.10 Sensitivity Analysis
22(1)
1.4 Conclusion
22(1)
References
23(2)
2 Saving Lives with Operations Research: Models to Improve HIV Resource Allocation
25(34)
Sabina S. Alistar
Margaret L. Brandeau
2.1 Introduction
25(6)
2.1.1 Background
25(2)
2.1.2 Modeling Approaches
27(4)
2.1.3
Chapter Overview
31(1)
2.2 HIV Resource Allocation: Theoretical Analyses
31(8)
2.2.1 Defining the Resource Allocation Problem
31(4)
2.2.2 Production Functions for Prevention and Treatment Programs
35(2)
2.2.3 Allocating Resources among Prevention and Treatment Programs
37(2)
2.3 HIV Resource Allocation: Portfolio Analyses
39(5)
2.3.1 Portfolio Analysis
39(1)
2.3.2 Opiate Substitution Therapy and ART in Ukraine
40(2)
2.3.3 Pre-exposure Prophylaxis and ART
42(2)
2.4 HIV Resource Allocation: A Tool for Decision Makers
44(6)
2.4.1 REACH Model Overview
44(1)
2.4.2 Example Analysis: Brazil
45(3)
2.4.3 Example Analysis: Thailand
48(2)
2.5 Discussion and Further Research
50(3)
Acknowledgment
53(1)
References
53(6)
3 Adaptive Decision-Making During Epidemics
59(22)
Reza Yaesoubi
Ted Cohen
3.1 Introduction
59(2)
3.2 Problem Formulation
61(2)
3.3 Methods
63(10)
3.3.1 The 1918 Influenza Pandemic in San Francisco, CA
63(1)
3.3.2 Stochastic Transmission Dynamic Models
64(2)
3.3.3 Calibration
66(3)
3.3.4 Optimizing Dynamic Health Policies
69(4)
3.4 Numerical Results
73(2)
3.5 Conclusion
75(1)
Acknowledgments
76(1)
References
76(5)
4 Assessing Register-Based Chlamydia Infection Screening Strategies: A Cost-Effectiveness Analysis on Screening Start/End Age and Frequency
81(28)
Yu Teng
Nan Kong
Wanzhu Tu
4.1 Introduction
81(2)
4.2 Background Literature Review
83(6)
4.2.1 Clinical Background on CT Infection and Control
83(2)
4.2.2 CT Screening Programs
85(1)
4.2.3 Computational Modeling on CT Transmission and Control
85(4)
4.3 Mathematical Modeling
89(9)
4.3.1 An Age-Structured Compartmental Model
89(4)
4.3.2 Model Parameterization and Validation
93(5)
4.4 Strategy Assessment
98(3)
4.4.1 Base-Case Assessment
98(2)
4.4.2 Sensitivity Analysis
100(1)
4.5 Conclusions and Future Research
101(1)
References
102(7)
5 Optimal Selection of Assays for Detecting Infectious Agents in Donated Blood
109(20)
Ebru K. Bish
Hadi El-Amine
Douglas R. Bish
Susan L. Stramer
Anthony D. Slonim
5.1 Introduction and Challenges
109(4)
5.1.1 Introduction
109(2)
5.1.2 The Challenges
111(2)
5.2 The Notation and Decision Problem
113(6)
5.2.1 Notation
114(1)
5.2.2 Measures of Interest
115(2)
5.2.3 Model Formulation
117(1)
5.2.4 Relationship of the Proposed Mathematical Models to Cost-Effectiveness Analysis
118(1)
5.3 The Case Study of the Sub-Saharan Africa Region and the United States
119(4)
5.3.1 Uncertainty in Prevalence Rates
122(1)
5.4 Contributions and Future Research Directions
123(1)
Acknowledgments
123(1)
References
124(5)
6 Modeling Chronic Hepatitis C During Rapid Therapeutic Advance: Cost-Effective Screening, Monitoring, and Treatment Strategies
129(24)
Shan Liu
6.1 Introduction
129(2)
6.2 Method
131(8)
6.2.1 Modeling Disease Natural History and Intervention
132(2)
6.2.2 Estimating Parameters for Disease Progression and Death
134(5)
6.3 Four Research Areas in Designing Effective HCV Interventions
139(9)
6.3.1 Cost-Effective Screening and Treatment Strategies
139(2)
6.3.2 Cost-Effective Monitoring Guidelines
141(1)
6.3.3 Optimal Treatment Adoption Decisions
141(4)
6.3.4 Optimal Treatment Delivery in Integrated Healthcare Systems
145(3)
6.4 Concluding Remarks
148(1)
References
148(5)
Part 2 Noncommunicable Disease Prevention 153(106)
7 Modeling Disease Progression and Risk-Differentiated Screening for Cervical Cancer Prevention
155(28)
Adriana Ley-Chavez
Julia L. Higle
7.1 Introduction
155(2)
7.2 Literature Review
157(2)
7.3 Modeling Cervical Cancer Screening
159(12)
7.3.1 Model Components
160(6)
7.3.2 Parameter Selection
166(3)
7.3.3 Implementation
169(2)
7.4 Model-Based Analyses
171(3)
7.4.1 Cost-Effectiveness Analysis
171(1)
7.4.2 Sensitivity Analysis
172(2)
7.5 Concluding Remarks
174(1)
References
175(8)
8 Using Finite-Horizon Markov Decision Processes for Optimizing Post-Mammography Diagnostic Decisions
183(18)
Sait Tunc
Oguzhan Alagoz
Jagpreet Chhatwal
Elizabeth S. Burnside
8.1 Introduction
183(2)
8.2 Model Formulations
185(3)
8.3 Structural Properties
188(5)
8.4 Numerical Results
193(3)
8.5 Summary
196(1)
Acknowledgments
196(1)
References
197(4)
9 Partially Observable Markov Decision Processes for Prostate Cancer Screening, Surveillance, and Treatment: A Budgeted Sampling Approximation Method
201(22)
Jingyu Zhang
Brian T. Denton
9.1 Introduction
201(3)
9.2 Review of POMDP Models and Benchmark Algorithms
204(2)
9.3 A POMDP Model for Prostate Cancer Screening, Surveillance, and Treatment
206(3)
9.4 Budgeted Sampling Approximation
209(4)
9.4.1 Lower and Upper Bounds
209(2)
9.4.2 Summary of the Algorithm
211(2)
9.5 Computational Experiments
213(4)
9.5.1 Finite-Horizon Test Instances
213(1)
9.5.2 Computational Experiments
214(3)
9.6 Conclusions
217(2)
References
219(4)
10 Cost-Effectiveness Analysis of Breast Cancer Mammography Screening Policies Considering Uncertainty in Women's Adherence
223(18)
Mahboubeh Madadi
Shengfan Zhang
10.1 Introduction
223(2)
10.2 Model Formulation
225(6)
10.3 Numerical Studies
231(2)
10.4 Results
233(3)
10.4.1 Perfect Adherence Case
233(1)
10.4.2 General Population Adherence Case
234(2)
10.5 Summary
236(1)
References
237(4)
11 An Agent-Based Model for Ideal Cardiovascular Health
241(18)
Yan Li
Nan Kong
Mark A. Lawley
Jose A. Pagan
11.1 Introduction
241(2)
11.2 Methodology
243(7)
11.2.1 Agent-Based Modeling
243(1)
11.2.2 Model Structure
244(2)
11.2.3 Parameter Estimation
246(2)
11.2.4 User Interface
248(1)
11.2.5 Model Validation
249(1)
11.3 Results
250(2)
11.3.1 Simulating American Adults
250(2)
11.4 Simulating the Medicare-Age Population and the Disease-Specific Subpopulations
252(2)
11.5 Future Research
254(1)
11.6 Summary
255(1)
References
255(4)
Part 3 Treatment Technology And System 259(142)
12 Biological Planning Optimization for High-Dose-Rate Brachytherapy and its Application to Cervical Cancer Treatment
261(24)
Eva K. Lee
Fan Yuan
Alistair Templeton
Rui Yao
Krystyna Kiel
James C.H. Chu
12.1 Introduction
261(2)
12.2 Challenges and Objectives
263(2)
12.3 Materials and Methods
265(8)
12.3.1 High-Dose-Rate Brachytherapy
265(1)
12.3.2 PET Image
266(1)
12.3.3 Novel OR-Based Treatment-Planning Model
266(5)
12.3.4 Computational Challenges and Solution Strategies
271(2)
12.4 Validation and Results
273(3)
12.5 Findings, Implementation, and Challenges
276(3)
12.6 Impact and Significance
279(2)
12.6.1 Quality of Care and Quality of Life for Patients
279(1)
12.6.2 Advancing the Cancer Treatment Frontier
279(1)
12.6.3 Advances in Operations Research Methodologies
280(1)
Acknowledgment
281(1)
References
281(4)
13 Fluence Map Optimization in Intensity-Modulated Radiation Therapy Treatment Planning
285(22)
Dionne M. Aleman
13.1 Introduction
285(3)
13.2 Treatment Plan Evaluation
288(4)
13.2.1 Physical Dose Measures
289(2)
13.2.2 Biological Dose Measures
291(1)
13.3 FMO Optimization Models
292(7)
13.3.1 Objective Functions
293(2)
13.3.2 Constraints
295(2)
13.3.3 Robust Formulation
297(2)
13.4 Optimization Approaches
299(1)
13.5 Conclusions
300(1)
References
301(6)
14 Sliding Window IMRT and VMAT Optimization
307(16)
David Craft
Tarek Halabi
14.1 Introduction
307(2)
14.2 Two-Step IMRT Planning
309(1)
14.3 One-Step IMRT Planning
310(3)
14.3.1 One-Step Sliding Window Optimization
310(3)
14.4 Volumetric Modulated ARC Therapy
313(2)
14.5 Future Work for Radiotherapy Optimization
315(2)
14.5.1 Custom Solver for Radiotherapy
315(1)
14.5.2 Incorporating Additional Hardware Considerations into Sliding Window VMAT Planning
315(1)
14.5.3 Trade-Off between Delivery Time and Plan Quality
316(1)
14.5.4 What Do We Optimize?
316(1)
14.6 Concluding Thoughts
317(1)
References
318(5)
15 Modeling the Cardiovascular Disease Prevention-Treatment Trade-Off
323(26)
George Miller
15.1 Introduction
323(2)
15.2 Methods
325(9)
15.2.1 Model Overview
325(2)
15.2.2 Model Structure
327(4)
15.2.3 Model Inputs
331(3)
15.3 Results
334(10)
15.3.1 Base Case
334(1)
15.3.2 Interaction between Prevention and Treatment Spending
335(1)
15.3.3 Impact of Discount Rate on Cost-Effectiveness
336(1)
15.3.4 Optimal Spending Mix
337(1)
15.3.5 Impact of Prevention Lag on Optimal Mix
338(2)
15.3.6 Impact of Discount Rate on Optimal Mix
340(1)
15.3.7 Impact of Time Horizon on Optimal Mix
340(1)
15.3.8 Impacts of Research
341(3)
15.4 Discussion
344(2)
Acknowledgment
346(1)
References
346(3)
16 Treatment Optimization for Patients with Type 2 Diabetes
349(18)
Jennifer Mason Lobo
16.1 Introduction
349(1)
16.2 Literature Review
350(3)
16.3 Model Formulation
353(4)
16.3.1 Decision Epochs
354(1)
16.3.2 States
354(1)
16.3.3 Actions
355(1)
16.3.4 Probabilities
355(1)
16.3.5 Rewards
356(1)
16.3.6 Value Function
356(1)
16.4 Numerical Results
357(5)
16.4.1 Model Inputs
357(1)
16.4.2 Optimal Treatment Policies to Reduce Polypharmacy
358(4)
16.5 Conclusions
362(1)
References
363(4)
17 Machine Learning for Early Detection and Treatment Outcome Prediction
367(34)
Eva K. Lee
17.1 Introduction
367(2)
17.2 Background
369(3)
17.3 Machine Learning with Discrete Support Vector Machine Predictive Models
372(8)
17.3.1 Modeling of Reserved-Judgment Region for General Groups
373(1)
17.3.2 Discriminant Analysis via Mixed-Integer Programming
374(2)
17.3.3 Model Variations
376(3)
17.3.4 Theoretical Properties and Computational Strategies
379(1)
17.4 Applying Damip to Real-World Applications
380(13)
17.4.1 Validation of Model and Computational Effort
381(1)
17.4.2 Applications to Biological and Medical Problems
381(8)
17.4.3 Applying DAMIP to UCI Repository of Machine Learning Databases
389(4)
17.5 Summary and Conclusion
393(1)
Acknowledgment
394(1)
References
394(7)
Index 401
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.