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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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ATCodeR: a dictionary-based R-tool to standardize medication free-text.

Isabel Schnorr1,2, Stefanie Andreas3,4, Linnea Schumann3

  • 1Faculty of Medicine, Institute for Digital Medicine and Clinical Data Sciences, Goethe University Frankfurt, Frankfurt, Germany. I.Schnorr@med.uni-frankfurt.de.

Scientific Reports
|April 10, 2025
PubMed
Summary

This study introduces an R-tool to structure oncology medication data from free text into the Anatomical Therapeutic Chemical (ATC) system. The tool achieved 88.5% accuracy, improving real-world data analysis for cancer research.

Keywords:
ATC codeDictionaryLanguage processingMedication dictionaryR-toolStandardizing free-textSubstance dictionary

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Area of Science:

  • Oncology
  • Medical Informatics
  • Clinical Data Science

Background:

  • Oncology treatment research is hindered by unstructured medication data in medical records.
  • Analyzing real-world data (RWD) for treatment patterns and outcomes requires structured information.
  • Clinical data science needs efficient tools for processing RWD in oncology.

Purpose of the Study:

  • To develop a user-friendly R-tool for transforming free-text medication entries into the Anatomical Therapeutic Chemical (ATC) Classification System.
  • To enhance the analysis of systemic anti-cancer treatments using structured RWD.
  • To improve the standardization and efficiency of oncology data analysis in German-speaking regions.

Main Methods:

  • A dictionary-based approach was employed within an R-tool to map free-text medication data to ATC codes.
  • The tool outputs a structured data frame with columns for antineoplastic and other medications.
  • Validation involved reviewing 561 data entries (935 tokens) from an evaluation dataset.

Main Results:

  • The R-tool successfully transformed 88.5% of tokens into their respective ATC codes.
  • Additional relevant information was extracted from 23% of the data entries.
  • Manual review identified that 8.9% of tokens failed transformation, with 4.1% yielding no usable information.

Conclusions:

  • The developed R-tool significantly improves the standardization and analysis of systemic anti-cancer treatment data.
  • This approach offers an efficient method for processing unstructured medication information in medical records.
  • The tool maintains relevant accuracy, facilitating more robust oncological research with real-world data.