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Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to

Rosy Tsopra1, Jean-Baptiste Lamy2, Karima Sedki2

  • 1LIMICS, INSERM UMRS 1142, Université Paris 13, Sorbonne Université, F-75006 Paris, France; AP-HP, Paris, France.

Artificial Intelligence in Medicine
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PubMed
Summary

This study introduces a novel preference learning method to automatically detect inconsistencies in clinical practice guidelines, particularly for antibiotic prescribing. The approach successfully identified numerous potential contradictions, aiding medical experts in validating critical issues in healthcare recommendations.

Keywords:
AntibiotherapyClinical practice guidelinesInconsistencies in guidelinesPreference learning

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Area of Science:

  • Medical Informatics
  • Clinical Decision Support
  • Artificial Intelligence in Healthcare

Background:

  • Clinical practice guidelines offer evidence-based recommendations but often contain contradictions and inconsistencies.
  • Examples include conflicting advice on antibiotic use, such as recommending sulfamethoxazole/trimethoprim for child sinusitis despite noted high bacterial resistance.

Purpose of the Study:

  • To propose and apply a semi-automatic method for detecting inconsistencies in clinical guidelines using preference learning.
  • To formalize therapeutic strategies in antibiotherapy using a preference model for improved guideline accuracy.

Main Methods:

  • Developed a preference learning model trained on guideline recommendations and a domain-specific knowledge base.
  • Applied the model to antibiotherapy in primary care, creating a generic model for various infectious diseases and patient profiles.
  • Utilized the model to identify potential inconsistencies within the guidelines.

Main Results:

  • The developed preference model successfully detected 106 candidate inconsistencies in clinical guidelines.
  • A medical expert validated 55 of these candidate inconsistencies, confirming the method's efficacy.
  • Demonstrated that therapeutic strategies in antibiotherapy guidelines can be effectively formalized by a preference model.

Conclusions:

  • Proposed an original preference-based approach for modeling clinical guidelines.
  • The method shows potential for integration into clinical decision support systems to assist physicians with antibiotic prescribing.
  • This approach can enhance the reliability and consistency of medical recommendations.