Machine Learning in Medicine - a Complete Overview

Inconsistent Spread (78 Patients).- Partial Correlations for Removing Interaction Effects from Efficacy Data (64 Patients).- Canonical Regression for Overall Statistics of Multivariate Data (250 Patients).- Multinomial Regression for Outcome Categories (55 Patients).- Various Methods for Analyzing Predictor Categories (60 and 30 Patients).- Random Intercept Models for Both Outcome and Predictor Categories (55 Patients).- Automatic Regression for Maximizing Linear Relationships (55 Patients).- Simulation Models for Varying Predictors (9000 Patients).- Generalized Linear Mixed Models for Outcome Prediction from Mixed Data (20 Patients).- Two Stage Least Squares for Linear Models with Problematic Predictors (35 Patients).- Autoregressive Models for Longitudinal Data (120 Monthly Population Records).- Variance Components for Assessing the Magnitude of Random Effects (40 Patients).- Ordinal Scaling for Clinical Scores with Inconsistent Intervals (900 Patients).- Loglinear Models for Assessing Incident Rates with Varying Incident Risks (12 Populations).- Loglinear Models for Outcome Categories (445 Patients).- Heterogeneity in Clinical Research: Mechanisms Responsible (20 Studies).- Performance Evaluation of Novel Diagnostic Tests (650 and 588 Patients).- Quantile - Quantile Plots, a Good Start for Looking at Your Medical Data (50 Cholesterol Measurements and 52 Patients).- Rate Analysis of Medical Data Better than Risk Analysis (52 Patients).- Trend Tests Will Be Statistically Significant if Traditional Tests Are not (30 and 106 Patients).- Doubly Multivariate Analysis of Variance for Multiple Observations from Multiple Outcome Variables (16 Patients).- Probit Models for Estimating Effective Pharmacological Treatment Dosages (14 Tests).- Interval Censored Data Analysis for Assessing Mean Time to Cancer Relapse (51 Patients).- Structural Equation Modeling with SPSS Analysis of Moment Structures (Amos) for Cause Effect Relationships I (35 Patients).- Structural Equation Modeling with SPSS Analysis of Moment Structures (Amos) for Cause Effect Relationships II (35 Patients).- Section III Rules Models.- Neural Networks for Assessing Relationships that are Typically Nonlinear (90 Patients). Complex Samples Methodologies for Unbiased Sampling (9,678 Persons).- Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple Groups (217 Patients).- Decision Trees for Decision Analysis (1004 and 953 Patients).-Multidimensional Scaling for Visualizing Experienced Drug Efficacies (14 Pain-killers and 42 Patients).- Stochastic Processes for Long Term Predictions from Short Term Observations.- Optimal Binning for Finding High Risk Cut-offs (1445 Families).- Conjoint Analysis for Determining the Most Appreciated Properties of Medicines to Be Developed (15 Physicians).- Item Response Modeling for Analyzing Quality of Life with Better Precision (1000 Patients).- Survival Studies with Varying Risks of Dying (50 and 60 Patients).- Fuzzy Logic for Improved Precision of Pharmacological Data Analysis (9 Induction Dosages).- Automatic Data Mining for the Best Treatment of a Disease (90 Patients).- Pareto Charts for Identifying the Main Factors of Multifactorial Outcomes (2000 Admissions to Hospital).- Radial Basis Neural Networks for Multidimensional Gaussian Data (90 persons).- Automatic Modeling for Drug Efficacy Prediction (250 Patients).- Automatic Modeling for Clinical Event Prediction (200 Patients).- Automatic Newton Modeling in Clinical Pharmacology (15 Alfentanil dosages, 15 Quinidine time-concentration relationships).- Spectral Plots for High Sensitivity Assessment of Periodicity (6 Years' Monthly C Reactive Protein Levels).- Runs Test for Identifying Best Analysis Models (21 Estimates of Quantity and Quality of Patient Care).- Evolutionary Operations for Health Process Improvement (8 Operation Room Settings).- Bayesian Networks for Cause Effect Modeling (600 Patients).- Support Vector Machines for Imperfect Nonlinear Data.- Multiple Response Sets for Visualizing Clinical Data Trends (811 Patient Visits).- Protein and DNA Sequence Mining.- Iteration Methods for Crossvalidation (150 Patients).- Testing Parallel-groups with Different Sample Sizes and Variances (5 Parallel-group Studies).- Association Rules between Exposure and Outcome (50 and 60 Patients).- Confidence Intervals for Proportions and Differences in Proportions (100 and 75 Patients).- Ratio Statistics for Efficacy Analysis of New Drugs 50 Patients).- Fifth Order Polynomes of Circadian Rhythms (1 Patient).- Gamma Distribution for Estimating the Predictors of Medical Outcomes (110 Patients). Index.