Kernel ridge regression in clinical research/ Ton J. Cleophas, Aeilko H. Zwinderman.
Cleophas, Ton J. M.; Zwinderman, Aeilko H.
2022
RA409
Available Online

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Title
Kernel ridge regression in clinical research/ Ton J. Cleophas, Aeilko H. Zwinderman.
Author
Cleophas, Ton J. M.
ISBN
9783031107177 (electronic bk.)
3031107179 (electronic bk.)
9783031107160
3031107160
DOI
https://doi.org/10.1007/978-3-031-10717-7
Publication Details
Cham : Springer, 2022.
Language
English
Description
1 online resource (292 p.)
Item Number
10.1007/978-3-031-10717-7 doi
Call Number
RA409
Dewey Decimal Classification
610.727
Summary
IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include the kernel trick for reduced arithmetic complexity, estimation of uncertainty by Gaussians unlike histograms, corrected data-overfit by ridge regularization, availability of 8 alternative kernel density models for datafit. A very exciting and wide array of preliminary KRR research has already been published by major disciplines (like studies in quantum mechanics and nuclear physics, studies of molecular affinity / dynamics, atomisation energy studies, but also forecasting economics studies, IoT (internet of things) studies for e-networks, plant stress response studies, big data streaming studies, etc). In contrast, it is virtually unused in clinical research. This edition is the first textbook and tutorial of kernel ridge regressions for medical and healthcare students as well as recollection / update bench, and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regression analyses. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment. We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology (2000-2002). From their expertise they should be able to make adequate selections of modern KRR methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 24 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.
Note
Chapter 9: Effect of Gender, Treatment, and Their Interactions on Numbers of Paroxysmal Atrial Fibrillations, 40 Patients, Tra...
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed September 28, 2022).
Added Author
Zwinderman, Aeilko H.
Available in Other Form
Kernel Ridge Regression in Clinical Research
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Intro
Preface
Contents
Chapter 1: Traditional Kernel Regression
1.1 Summary
1.2 Introduction
1.3 Kernel Regression
1.4 Conclusion
1.5 References
Chapter 2: Kernel Ridge Regression (KRR)
2.1 Summary
2.2 History of Kernel Ridge Regression
2.3 Kernel Density Modeling
2.4 The Kernel Trick
2.5 Ridge Regularization
2.6 Conclusion
2.7 References
Chapter 3: Optimal Scaling vs Kernel Ridge Regression
3.1 Summary
3.1.1 Summaries of the Traditional Linear Regressions
3.1.2 Summaries of the Kernel Ridge Regressions
3.1.3 In Conclusion

3.2 Introduction
3.3 Optimal Scaling
3.4 Traditional Regressions
3.5 Kernel Ridge Regressions Scale 1
3.6 Kernel Ridge Regressions Scale 2
3.7 Kernel Ridge Regressions Scale 3
3.8 Conclusion
3.8.1 Summary of the Traditional R Square Values of the Scales 1-3 Models
3.8.2 Summary of Kernel Ridge Regressions (KRR)
3.9 References
Chapter 4: Examples of Published Kernel Ridge Regressions So Far
4.1 Summary
4.2 Introduction
4.3 History of Kernel Ridge Regression
4.4 A Brief Search of Kernel Ridge Regression Publications So Far

4.5 Courses Where the Upcoming Edition ``Kernel Ridge Regression in Clinical Research ́́Will Be Used
4.6 Conclusion
4.7 References
Chapter 5: Some Terminology
5.1 Summary
5.2 Alphabetical Enumeration
5.3 References
Chapter 6: Effect on Being Blind of Age/Sex Adjusted Mortality of Onchocerciasis Patients in 12,816 Personyears, Traditional v...
6.1 Summary
6.1.1 Summaries of Traditional Regressions
6.1.2 Summaries of Kernel Ridge Regressions
6.2 Introduction
6.3 Data Example
6.4 Traditional Linear Regression
6.5 Kernel Ridge Regressions
6.6 Conclusion

6.6.1 Summaries of Traditional Regressions
6.6.2 Summaries of Kernel Ridge Regressions
6.7 References
Chapter 7: Effect of Old Treatment on New Treatment, 35 Patients, Traditional Regressions vs Kernel Ridge Regressions
7.1 Summary
7.1.1 Summaries of Traditional Regressions
7.1.2 Summaries of Kernel Ridge Regressions
7.2 Introduction
7.3 Data Example
7.4 Traditional Linear Regression
7.5 Robust Regression
7.6 Quantile Regressions
7.7 Kernel Ridge Regression
7.8 Conclusion
7.8.1 Summaries of Traditional Regressions

7.8.2 Summaries of Kernel Ridge Regressions
7.9 References
Chapter 8: Effect of Gene Expressions on Drug Efficacy, 250 Patients, Traditional Regressions vs Kernel Ridge Regression
8.1 Summary
8.1.1 Summaries of Traditional Regressions
8.1.2 Summaries of Kernel Ridge Regressions
8.2 Introduction
8.3 Data Example
8.4 Traditional Linear Regression
8.5 Kernel Ridge Regression
8.6 Conclusion
8.6.1 Summaries of Traditional Regressions
8.6.2 Summaries of Kernel Ridge Regressions
8.7 References

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