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Related Experiment Videos

Extending the GEM model to support knowledge extraction from textual guidelines.

Gersende Georg1, Brigitte Séroussi, Jacques Bouaud

  • 1SPIM, Inserm ERM 202, Université Paris 6, 15 Rue de l'Ecole de Médecine, F-75006 Paris, France. gersende.georg@spim.jussieu.fr

International Journal of Medical Informatics
|February 8, 2005
PubMed
Summary

Computerizing clinical practice guidelines (CPGs) improves physician compliance. An extended Guideline Elements Model (GEM) enables automatic rule base generation from textual guidelines, outperforming manual methods.

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Area of Science:

  • Medical Informatics
  • Health Informatics
  • Computerized Clinical Decision Support

Background:

  • Paper-based Clinical Practice Guidelines (CPGs) have limited impact on medical practice.
  • Computerization of CPGs is proposed to enhance dissemination and physician adherence.
  • The Guideline Elements Model (GEM) aids in encoding CPGs for automatic processing.

Purpose of the Study:

  • To explore the automatic generation of a rule base from textual guidelines using GEM.
  • To propose an extension of the GEM model for more efficient decision process representation.
  • To facilitate the automatic derivation of decision rules from CPGs.

Main Methods:

  • An extended GEM model with decision variable structuring was developed.
  • Canadian hypertension management recommendations (1999) were encoded using the extended GEM.

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  • An XML parser extracted elements to generate IF-THEN decision rules.

    • Main Results:

    • An extended GEM model enabled efficient representation of decision processes.
    • A rule base was automatically generated from the GEM-encoded hypertension guidelines.
    • The automatically generated rule base showed favorable comparison with manually generated rules.

    Conclusions:

    • The extended GEM model facilitates automatic rule base generation from textual CPGs.
    • This approach supports the computerization of CPGs throughout their lifecycle.
    • Automatic rule generation offers a viable alternative to manual methods for CPG implementation.