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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Cleophas, Ton J. (Autor), Zwinderman, Aeilko H. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2014.
Edición:1st ed. 2014.
Colección:SpringerBriefs in Statistics,
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.