Machine Learning in Medicine - a Complete Overview /
| Autores principales: | , |
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| Autor Corporativo: | |
| Formato: | eBook |
| Lenguaje: | English |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Edición: | 1st ed. 2015. |
| Materias: |
Tabla de Contenidos:
- Preface. Section I Cluster and Classification Models
- Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys (50 Patients)
- Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data (50 Patients)
- Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future Patients (120 Patients)- Nearest Neighbors for Classifying New Medicines (2 New and 25 Old Opioids)- Predicting High-Risk-Bin Memberships (1445 Families)
- Predicting Outlier Memberships (2000 Patients)
- Data Mining for Visualization of Health Processes (150 Patients)
- 8 Trained Decision Trees for a More Meaningful Accuracy (150 Patients)
- Typology of Medical Data (51 Patients)
- Predictions from Nominal Clinical Data (450 Patients)
- Predictions from Ordinal Clinical Data (450 Patients)
- Assessing Relative Health Risks (3000 Subjects)
- Measurement Agreements (30 Patients)
- Column Proportions for Testing Differences between Outcome Scores (450 Patients)
- Pivoting Trays and Tables for Improved Analysis of Multidimensional Data (450 Patients)
- Online Analytical Procedure Cubes for a More Rapid Approach to Analyzing Frequencies (450 Patients)
- Restructure Data Wizard for Data Classified the Wrong Way (20 Patients).- Control Charts for Quality Control of Medicines (164 Tablet Disintegration Times)
- Section II (Log) Linear Models
- Linear, Logistic, and Cox Regression for Outcome Prediction with Unpaired Data (20, 55, and 60 Patients).- Generalized Linear Models for Outcome Prediction with Paired Data (100 Patients and 139 Physicians)
- Generalized Linear Models for Predicting Event-Rates (50 Patients).- Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction (250 Patients)
- Optimal Scaling of High-sensitivity Analysis of Health Predictors (250 Patients)
- Discriminant Analysis for Making a Diagnosis from Multiple Outcomes (45 Patients)
- Weighted Least Squares for Adjusting Efficacy Data with 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.