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From Free-text Drug Labels to Structured Medication Terminology with BERT and GPT.

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We developed a method to extract medication details from free-text drug labels using Named Entity Recognition (NER) and knowledge graphs. This approach enhances structured medication information retrieval and management in healthcare systems.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Pharmacology

Background:

  • Controlled medication terminology lacks comprehensive data found in free-text drug labels.
  • Efficiently structuring medication information from unstructured text is crucial for healthcare.

Purpose of the Study:

  • To develop and evaluate a method for enriching controlled medication terminology using free-text drug labels.
  • To identify the most effective Named Entity Recognition (NER) model for extracting medication entities.

Main Methods:

  • Compared various NER models (rule-based, deep learning, Transformers, GPT-3, ChatGPT) for medication entity extraction.
  • Utilized a rule-based Relation Extraction algorithm to build a medication knowledge graph.
  • Developed a Medication Searching method to match extracted information with a terminology server.

Main Results:

  • BERT-CRF demonstrated the highest effectiveness as an NER model, achieving an F-measure of 95%.
  • The Medication Searching method, after term normalization, reached 77% accuracy in matching labels to relevant medications.
  • The developed models can be deployed as a web service for structured medication information retrieval.

Conclusions:

  • The proposed method effectively enriches controlled medication terminology from free-text sources.
  • This approach improves the management and accessibility of medication information across different health systems.