Utvidet returrett til 31. januar 2024

Machine Learning in Medicine - A Complete Overview

Om 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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  • Språk:
  • Engelsk
  • ISBN:
  • 9783030339692
  • Bindende:
  • Hardback
  • Sider:
  • 667
  • Utgitt:
  • 4. mars 2020
  • Utgave:
  • 22020
  • Dimensjoner:
  • 155x235x0 mm.
  • Vekt:
  • 1208 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 28. november 2024

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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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