Ebook: Machine Learning in Medicine

V.1. Introduction to machine learning -- Logistic regression for health profiling -- Optimal scaling: discretization -- Optimal scaling: regulaization including ridge, lasso, and elastic net regression -- Partial correlations -- Mixed linear models -- Binary partitioning -- Item response modeling -- Time-dependent predictor modeling -- Seasonality assessments -- Non-linear modeling -- Artificial intelligence, multilayer perceptron modeling -- Artificial intelligence, radial basis functions -- Factor analysis -- Hierarchical cluster analysis for unsupervised data -- Partial least squares -- Discriminant analysis for supervised data -- Canonical regression -- Fuzzy modeling -- Conclusions--;"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"--Publisher's description.;v. 2. Introduction to machine learning part two -- Two-stage least squares -- Multiple imputations -- Bhattacharya analysis -- Quality-of-life (QOL) assessments with odds ratios -- Logistic regression for assessing novel diagnostic tests against control -- validating surrogate endpoints -- Two-dimensional clustering -- Multidimensional clustering -- Anomaly detection -- Association rule analysis -- Multidimensional scaling -- Correspondence analysis -- Multivariate analysis of time series -- Support vector machines -- Bayesian networks -- Protein and DNA sequence mining -- Continuous sequential techniques -- Discrete wavelet analysis -- Machine learning and common sense.;v. 3. Introduction to Machine learning part three -- Evolutionary operations -- Multiple treatments -- Multiple endpoints -- Optimal binning -- Exact p-values -- Probit regression -- Over-dispersion -- Random effects -- Weighted least squares -- Multiple response sets -- Complex samples -- Runs tests -- Decision trees -- Spectral plots -- Newton's methods -- Stochastic processes, stationary Markov chains -- Stochastic processes, absorbing Markov chains -- Conjoint models -- Machine learning and unsolved questions.