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Kernel ridge regression in clinical research

Record Type:	Electronic resources : Monograph/item
Title/Author:	Kernel ridge regression in clinical researchby Ton J. Cleophas, Aeilko H. Zwinderman.
Author:	Cleophas, Ton J. M.
other author:	Zwinderman, Aeilko H.
Published:	Cham :Springer International Publishing :2022.
Description:	xvii, 289 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Medical statistics.
Online resource:	https://doi.org/10.1007/978-3-031-10717-7
ISBN:	9783031107177$q(electronic bk.)

Kernel ridge regression in clinical research
Cleophas, Ton J. M.

Kernel ridge regression in clinical research[electronic resource] /by Ton J. Cleophas, Aeilko H. Zwinderman. - Cham :Springer International Publishing :2022. - xvii, 289 p. :ill., digital ;24 cm.

1. Traditional Kernel Regression -- 2. Kernel Ridge Regression -- 3. Optimal Scaling vs Kernel Ridge Regression -- 4. Examples of Published Kernel Ridge Regression Research So Far -- 5. Some Terminology -- 6. Effect of Being Blind on Age/Sex Adjusted Mortality Rate, 11630 Patients, Traditional Regressions vs Kernel Ridge Regression -- 7. Effect of Old Treatment on New Treatment, 35 patients, Traditional Regression vs Kernel Ridge Regression -- 8. Effect of Gene Expressions on Drug Efficacy, 250 Patients, Traditional Regressions vs Kernel Ridge Regression -- 9. Effect of Gender, Treatment, and Their Interaction on Numbers of Paroxysmal Atrial Fibrillations, 40 Patients, Traditional Regressions vs Kernel Ridge Regression -- 10. Effect of Laboratory Predictors on Septic Mortality, 200 Patients, Traditional Regressions vs Kernel Ridge Regression -- 11. Effect of Times on C-reactive Protein Levels, 18 Months, Traditional Regressions vs Kernel Ridge Regression -- 12. Effect of Different Dosages of Prednisone and Beta-agonist on Peakflow, 78 Patients, Traditional Regressions vs Kernel Ridge Regression -- 13. Effect of Race, Age, and Gender on Physical Strength, 60 Patients, Traditional Regressions vs Kernel Ridge Regression -- 14. Effect of Treatment, Age, Gender, and Co-morbidity on Hours of Sleep, 20 Patients, Traditional Regressions vs Kernel Ridge Regression -- 15. Effect of Counseling Frequency and Non-compliance on Monthly Stools, 35 Constipated Patients, Traditional Regressions vs Kernel Ridge Regression -- 16. Effect of Treatment Modality, Counseling, and Satisfaction with Doctor on Quality of Life, 450 Patients, Traditional Regressions vs Kernel Ridge Regression -- 17. Effect of Department and Patient Age Class on Risk of Falling out of Bed, 55 Patients, Traditional Regressions vs Kernel Ridge Regression -- 18. Effect of Diet, Gender, Sport, and Medical Treatment on LDL Cholesterol Reduction, 953 Patients, Traditional Regressions vs Kernel Ridge Regression -- 19. Effect of Gender, Age, Weight, and Height on Measured Body Surface, 90 Patients, Traditional Regressions vs Kernel Ridge Regression -- 20. Effect of General Practitioners' Age, Education, and Type of Practice on Lifestyle Advise Given, 139 Physicians, Traditional Regressions vs Kernel Ridge Regression -- 21. Effect of Treatment, Psychological, and Social Scores on numbers of Paroxysmal Atrial Fibrillations, 50 Patients, Traditional Regressions vs Kernel Ridge Regression -- 22. Effects of Various Predictors on Numbers of Convulsions, 3390 Patients, Traditional vs Kernel Ridge Regression -- 23. Effects of Foods Served on Breakfast Taken, 252 Persons, Traditional Regressions and Multinomial Logistic Regression vs Kernel Ridge Regression -- 24. Effect on Anorexia of Personal Factors, 217 Persons, Traditional Regression vs Kernel Ridge Regression -- 25. Effect of Physical Exercise, Calorie Intake, and Their Interaction, on Weight Loss, 64 Patients, Traditional Regressions vs Kernel Ridge Regression -- 26. Summaries.

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.

ISBN: 9783031107177$q(electronic bk.)

Standard No.: 10.1007/978-3-031-10717-7doiSubjects--Topical Terms:

182007
Medical statistics.

LC Class. No.: RA409 / .C54 2022

Dewey Class. No.: 610.727

Kernel ridge regression in clinical research
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520 $a 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.
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