Machine Learning in Medicine
Nonfiction, Science & Nature, Science, Biological Sciences, Entomology, Mathematics, Statistics, Health & Well Being, Medical
| Author: | Ton J. Cleophas, Aeilko H. Zwinderman | ISBN: | 9789400758247 |
| Publisher: | Springer Netherlands | Publication: | February 12, 2013 |
| Imprint: | Springer | Language: | English |
| Author: | Ton J. Cleophas, Aeilko H. Zwinderman |
| ISBN: | 9789400758247 |
| Publisher: | Springer Netherlands |
| Publication: | February 12, 2013 |
| Imprint: | Springer |
| Language: | English |
Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.
Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.
