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Machine Learning in Medicine - A Complete Overview

About Machine Learning in Medicine - A Complete Overview

Contents Preface Section I Cluster and Classification Models 1 Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys (50 Patients) 2 Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data (50 Patients) 3 Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future Patients (120 Patients) 4 Nearest Neighbors for Classifying New Medicines (2 New and 25 Old Opioids) 5 Predicting High-Risk-Bin Memberships (1445 Families) 6 Predicting Outlier Memberships (2000 Patients) 7 Data Mining for Visualization of Health Processes (150 Patients) 8 Trained Decision Trees for a More Meaningful Accuracy (150 Patients) 9 Typology of Medical Data (51 Patients) 10 Predictions from Nominal Clinical Data (450 Patients)

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  • Language:
  • English
  • ISBN:
  • 9783030339692
  • Binding:
  • Hardback
  • Pages:
  • 667
  • Published:
  • March 3, 2020
  • Edition:
  • 22020
  • Dimensions:
  • 155x235x0 mm.
  • Weight:
  • 1208 g.
Delivery: 2-3 weeks
Expected delivery: December 12, 2024

Description of Machine Learning in Medicine - A Complete Overview

Contents
Preface
Section I Cluster and Classification Models
1 Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys (50 Patients)
2 Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data (50 Patients)

3 Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future Patients (120 Patients)
4 Nearest Neighbors for Classifying New Medicines (2 New and 25 Old Opioids)
5 Predicting High-Risk-Bin Memberships (1445 Families)

6 Predicting Outlier Memberships (2000 Patients)

7 Data Mining for Visualization of Health Processes (150 Patients)

8 Trained Decision Trees for a More Meaningful Accuracy (150 Patients)
9 Typology of Medical Data (51 Patients)

10 Predictions from Nominal Clinical Data (450 Patients)

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