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Predictive data mining in clinical medicine: current issues and guidelines.

Riccardo Bellazzi1, Blaz Zupan

  • 1Dipartimento di Informatica e Sistemistica, Università di Pavia, via Ferrata 1, 27100 Pavia, Italy. Riccardo.Bellazzi@unipv.it

International Journal of Medical Informatics
|December 26, 2006
PubMed
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Predictive data mining offers essential tools for clinical prediction. This review provides guidelines for selecting, building, and validating data mining models in medicine, aiding researchers and practitioners.

Area of Science:

  • Medical Informatics
  • Computational Medicine
  • Clinical Data Science

Background:

  • Advancements in computational methods necessitate systematic approaches for clinical prediction problems.
  • Data mining offers solutions for medical data analysis and predictive model construction.
  • Guidelines are needed for selecting, building, validating, and disseminating data mining models in clinical settings.

Purpose of the Study:

  • To review the field of predictive data mining in clinical medicine.
  • To propose a framework for constructing, assessing, and utilizing data mining models.
  • To address challenges in applying data mining within clinical environments.

Main Methods:

  • Systematic review of recent research in predictive data mining for clinical medicine.

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  • Identification of critical issues and common challenges.
  • Synthesis of approaches into actionable lessons learned.

    • Main Results:

    • Comprehensive overview of the current state-of-the-art in clinical predictive data mining.
    • Provision of practical guidelines for conducting data mining studies in medicine.
    • Highlighting the importance of standardized procedures for model deployment.

    Conclusions:

    • Predictive data mining is crucial for medical research and practice.
    • Standardized procedures are mandatory for effective deployment and dissemination of results.
    • Genomic medicine integration presents new opportunities and complex challenges for predictive data mining.