Pardalos / Romeijn Handbook of Optimization in Medicine

1. Auflage 2014
ISBN: 978-0-387-09770-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 26, 442 Seiten

Reihe: Springer Optimization and Its Applications

Handbook of Optimization in Medicine is devoted to examining the dramatic increase in the application of effective optimization techniques to the delivery of health care. The articles, written by experts, focus on models and algorithms that have led to more efficient and sophisticated treatments of patients. Topics covered include: optimization in medical imaging, classification and data mining with medical applications, treatment of epilepsy and other brain disorders, treatment of head-and-neck, prostate, and other cancers using conventional conformal and intensity-modulated radiation therapy as well as proton therapy, treatment selection for breast cancer based on new classification schemes, optimization for the genome project, optimal timing of organ transplants.

Autoren/Hrsg.

Pardalos, Panos M.

Romeijn, H. Edwin

Inhaltsverzeichnis

1;Preface;7
2;Contents;9
3;List of Contributors;11
4;1 Optimizing Organ Allocation and Acceptance;14
4.1;1.1 Introduction;14
4.2;1.2 Kidney Allocation System;16
4.3;1.3 Liver Allocation System;17
4.4;1.4 Optimization from the Patient’s Perspective;20
4.5;1.5 Optimization from the Societal Perspective;25
4.6;1.6 Conclusions;35
4.7;Acknowledgments;35
4.8;References;35
5;2 Can We Do Better? Optimization Models for Breast Cancer Screening;38
5.1;2.1 Introduction;38
5.2;2.2 Optimization Models for Mammography Screening;43
5.3;2.3 Models for Scheduling Screening Examinations;48
5.4;2.4 Optimization Models for Breast Cancer Screening (and Treatment);57
5.5;2.5 Areas for Future Research;60
5.6;Acknowledgments;63
5.7;References;63
6;3 Optimization Models and Computational Approaches for Three-dimensional Conformal Radiation Treatment Planning;66
6.1;3.1 Introduction;66
6.2;3.2 Three-dimensional Conformal Radiation Therapy;68
6.3;3.3 Formulating the Optimization Problems;72
6.4;3.4 Solution Quality in Clinical Perspective;78
6.5;3.5 Solution Time Reduction Techniques;84
6.6;3.6 Case Study;87
6.7;3.7 Discussion;91
6.8;References;91
7;4 Continuous Optimization of Beamlet Intensities for Intensity Modulated Photon and Proton Radiotherapy;95
7.1;4.1 Introduction;95
7.2;4.2 Preliminaries;99
7.3;4.3 Optimization Models for IMRT Treatment Planning;101
7.4;4.4 Sensitivity Analysis;119
7.5;4.5 Intensity Modulated Proton Therapy;121
7.6;4.6 Example Case;123
7.7;Acknowledgments;126
7.8;References;126
8;5 Multicriteria Optimization in Intensity Modulated Radiotherapy Planning;135
8.1;5.1 The IMRT Treatment Planning Problem;135
8.2;5.2 Optimization as a Virtual Engineering Process;139
8.3;5.3 Multicriteria Optimization;145
8.4;5.4 The Numerical Realization;156
8.5;5.5 Navigating the Database;160
8.6;5.6 Clinical Examples;169
8.7;5.7 Research Topics;173
8.8;Acknowledgment;174
8.9;References;174
9;6 Algorithms for Sequencing Multileaf Collimators;180
9.1;6.1 Introduction;180
9.2;6.2 Algorithms for SMLC;184
9.3;6.3 Algorithms for DMLC;199
9.4;6.4 Field Splitting Without Feathering;207
9.5;6.5 Minimizing the Number of Segments;217
9.6;6.6 Conclusion;221
9.7;Acknowledgment;221
9.8;References;222
10;7 Image Registration and Segmentation Based on Energy Minimization;224
10.1;7.1 Image Registration;224
10.2;7.2 Edge Detection and Image Segmentation;242
10.3;Acknowledgment;259
10.4;References;259
11;8 Optimization Techniques for Data Representations with Biomedical Applications;264
11.1;8.1 Introduction;264
11.2;8.2 Independent Component Analysis;265
11.3;8.3 Other Methods for ICA;277
11.4;8.4 Sparse Component Analysis and Blind Source Separation Using Sparseness;280
11.5;8.5 Applications;287
11.6;8.6 Conclusion;298
11.7;Acknowledgments;298
11.8;References;298
12;9 Algorithms for Genomics Analysis;302
12.1;9.1 Introduction;302
12.2;9.2 Phylogenetic Analysis;303
12.3;9.3 Multiple Sequence Alignment;311
12.4;9.4 Novel Graph-Theoretical–Based Genomic Models;318
12.5;9.5 Summary;329
12.6;Acknowledgment;330
12.7;References;330
13;10 Optimization and Data Mining in Epilepsy Research: A Review and Prospective;335
13.1;10.1 Introduction;335
13.2;10.2 Background: Epilepsy and Seizure Prediction;336
13.3;10.3 Mining EEG Time Series: Chaos in Brain;341
13.4;10.4 Optimization and Data Mining in Epilepsy Research;343
13.5;10.5 Concluding Remarks and Prospective Issues;358
13.6;Acknowledgments;360
13.7;References;360
14;11 Mathematical Programming Approaches for the Analysis of Microarray Data;367
14.1;11.1 Microarrays and the New Biology;367
14.2;11.2 Issues in Microarray Data Analysis;368
14.3;11.3 Analysis of Gene Expression Data: Tissue Clustering and Classification;369
14.4;11.4 Inferring Regulatory Networks;380
14.5;11.5 A Final Comment;382
14.6;11.6 Research Challenges;382
14.7;Acknowledgments;385
14.8;References;385
15;12 Classification and Disease Prediction via Mathematical Programming;390
15.1;12.1 Introduction;391
15.2;12.2 Mathematical Programming Approaches;395
15.3;12.3 MIP-Based Multigroup Classification Models and Applications to Medicine and Biology;410
15.4;12.4 Progress and Challenges;428
15.5;12.5 Other Methods;428
15.6;12.6 Summary and Conclusion;430
15.7;Acknowledgment;432
15.8;References;432
16;Index;440