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Clustering Clinical Data in R.

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  • 1Centro de Estudos de Doenças Crónicas (CEDOC), NOVA Medical School-Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisbon, Portugal. ana.pina@nms.unl.pt.

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Summary
This summary is machine-generated.

This chapter explores cluster analysis for clinical data using R, a powerful data mining technique. It aids in understanding complex patterns for advancing precision medicine and clinical research.

Keywords:
Clinical dataCluster analysisCluster optimizationCluster stabilityCluster tendencyCluster validationStratification

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Clinical data analysis

Background:

  • The shift towards precision medicine necessitates advanced analytical techniques.
  • Data mining, particularly cluster analysis, is crucial for integrating complex biological and clinical data.
  • Understanding pathological mechanisms requires exploring patterns beyond human perception.

Purpose of the Study:

  • To provide a comprehensive overview of cluster analysis for clinical data.
  • To guide users through the process using the R statistical software.
  • To highlight practical applications and validation methods in clinical settings.

Main Methods:

  • Data preprocessing techniques for clinical datasets.
  • Discussion of various clustering algorithms, including their pros and cons.
  • Demonstration of R packages for implementing cluster analysis steps.

Main Results:

  • Outlines the essential steps in performing cluster analysis on clinical data.
  • Identifies suitable R packages for each stage of the analysis.
  • Provides examples of clinical applications and cluster validation strategies.

Conclusions:

  • Cluster analysis is a vital tool for uncovering hidden patterns in clinical data.
  • R offers a robust environment for executing and validating clustering methods.
  • This approach supports the transition to precision medicine by enhancing data interpretation.