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Finding medication doses in the liteature.

Dina Demner-Fushman1, James G Mork1, Willie J Rogers1

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Summary
This summary is machine-generated.

Extracting medication doses from scientific literature is challenging. Full-text analysis is crucial, and new methods improve drug dose extraction accuracy from research papers.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Pharmacology

Background:

  • Medication doses are critical for drug safety and efficacy.
  • Dose information is primarily available as free text in scientific literature.
  • Existing systems for clinical text may not effectively extract doses from research articles.

Purpose of the Study:

  • To evaluate existing drug prescription information extraction systems on scientific literature.
  • To investigate the efficacy of sequence-to-sequence neural networks for dose extraction.
  • To identify challenges in extracting medication doses from scientific texts.

Main Methods:

  • Developed a dataset of 694 PubMed Central documents annotated for drug dose information using the i2b2 schema.
  • Compared performance of existing systems with novel sequence-to-sequence models.
  • Analyzed differences in scope and formatting between clinical and literature dose information.

Main Results:

  • Less than half of all drug doses were found in MEDLINE/PubMed abstracts; full-text access is essential.
  • New approaches are needed due to distinct characteristics of dose information in literature.
  • Achieved 83.9% recall, 87.2% precision, and 85.5% F1 score for complete drug prescription extraction.

Conclusions:

  • Scientific literature contains significant drug dose information not present in abstracts.
  • Sequence-to-sequence models show promise for improving drug dose extraction from research articles.
  • Tailored methods are required to accurately capture medication dosage from diverse scientific texts.