Machine Learning in Medicine - Cookbook
The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys a...
Clasificación: | Libro Electrónico |
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Autores principales: | , |
Autor Corporativo: | |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
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Edición: | 1st ed. 2014. |
Colección: | SpringerBriefs in Statistics,
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Temas: | |
Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- I Cluster 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)
- II 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) Exact P-Values
- 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). 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)
- Index.