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Smoking Status Normalization with Cross-Encoders and SNOMED CT.

Akhila Abdulnazar1, Stefan Schulz1, Markus Kreuzthaler1

  • 1Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.

Studies in Health Technology and Informatics
|May 17, 2025
PubMed
Summary
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This study standardized smoking status documentation in German clinical notes using a bi-encoder and cross-encoder model. The method achieved 85% accuracy in mapping narrative terms to SNOMED CT codes for better healthcare data consistency.

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Clinical Documentation

Background:

  • Accurate smoking status documentation is crucial for patient care and clinical decisions.
  • Smoking status is frequently embedded within unstructured clinical narratives.
  • Standardizing clinical data enhances interoperability and consistency across healthcare systems.

Purpose of the Study:

  • To normalize smoking status mentions in German clinical narratives.
  • To map narrative expressions to standardized SNOMED CT codes.
  • To improve the consistency and usability of smoking status data in electronic health records.

Main Methods:

  • Utilized a bi-encoder and cross-encoder re-ranking model for natural language processing.
  • Developed a system to identify and normalize smoking-related terms in clinical text.
Keywords:
Medical Concept NormalizationSNOMED CTSmoking Status

Related Experiment Videos

  • Assigned SNOMED CT codes to standardize identified smoking status information.

    • Main Results:

    • Achieved 85% accuracy for Recall@1 in mapping narrative expressions to SNOMED CT definitions.
    • Successfully standardized smoking status information from unstructured clinical narratives.
    • Demonstrated the model's effectiveness for German-language clinical data.

    Conclusions:

    • The bi-encoder and cross-encoder model effectively standardizes smoking status documentation from clinical narratives.
    • SNOMED CT coding enhances the consistency and clinical utility of smoking status data.
    • This approach offers a scalable solution for improving clinical documentation quality.