Ebook: Machine Learning in Medicine

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.

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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.

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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.
Content:
Front Matter....Pages i-xv
Introduction to Machine Learning....Pages 1-15
Logistic Regression for Health Profiling....Pages 17-24
Optimal Scaling: Discretization....Pages 25-38
Optimal Scaling: Regularization Including Ridge, Lasso, and Elastic Net Regression....Pages 39-53
Partial Correlations....Pages 55-64
Mixed Linear Models....Pages 65-77
Binary Partitioning....Pages 79-86
Item Response Modeling....Pages 87-98
Time-Dependent Predictor Modeling....Pages 99-111
Seasonality Assessments....Pages 113-126
Non-linear Modeling....Pages 127-143
Artificial Intelligence, Multilayer Perceptron Modeling....Pages 145-156
Artificial Intelligence, Radial Basis Functions....Pages 157-166
Factor Analysis....Pages 167-181
Hierarchical Cluster Analysis for Unsupervised Data....Pages 183-195
Partial Least Squares....Pages 197-213
Discriminant Analysis for Supervised Data....Pages 215-224
Canonical Regression....Pages 225-240
Fuzzy Modeling....Pages 241-253
Conclusions....Pages 255-257
Back Matter....Pages 259-265

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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.
Content:
Front Matter....Pages i-xv
Introduction to Machine Learning....Pages 1-15
Logistic Regression for Health Profiling....Pages 17-24
Optimal Scaling: Discretization....Pages 25-38
Optimal Scaling: Regularization Including Ridge, Lasso, and Elastic Net Regression....Pages 39-53
Partial Correlations....Pages 55-64
Mixed Linear Models....Pages 65-77
Binary Partitioning....Pages 79-86
Item Response Modeling....Pages 87-98
Time-Dependent Predictor Modeling....Pages 99-111
Seasonality Assessments....Pages 113-126
Non-linear Modeling....Pages 127-143
Artificial Intelligence, Multilayer Perceptron Modeling....Pages 145-156
Artificial Intelligence, Radial Basis Functions....Pages 157-166
Factor Analysis....Pages 167-181
Hierarchical Cluster Analysis for Unsupervised Data....Pages 183-195
Partial Least Squares....Pages 197-213
Discriminant Analysis for Supervised Data....Pages 215-224
Canonical Regression....Pages 225-240
Fuzzy Modeling....Pages 241-253
Conclusions....Pages 255-257
Back Matter....Pages 259-265
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