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MetaMap Lite in Excel: Biomedical Named-Entity Recognition for Non-Technical Users.

Ravi Teja Bhupatiraju1, Kin Wah Fung1, Olivier Bodenreider1

  • 1National Library of Medicine, Bethesda, MD, USA.

Studies in Health Technology and Informatics
|January 4, 2018
PubMed
Summary

Researchers can now easily perform Named-Entity Recognition (NER) on biomedical text using a new, user-friendly spreadsheet tool. This system, a front-end for MetaMap Lite, offers a quick start for incorporating NER into research investigations.

Keywords:
Natural language processingUnified medical language system

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing

Background:

  • Biomedical researchers often require sophisticated tools for text analysis.
  • Named-Entity Recognition (NER) is crucial for extracting information from unstructured biomedical text.
  • Existing NER tools can have a steep learning curve for non-technical users.

Purpose of the Study:

  • To develop an accessible tool for biomedical Named-Entity Recognition (NER).
  • To provide a user-friendly interface for non-technical researchers to perform NER.
  • To integrate NER capabilities within a familiar spreadsheet environment.

Main Methods:

  • Development of an easy-to-use, offline, end-user front-end application.
  • Integration with MetaMap Lite for NER.
  • Deployment in a standard spreadsheet interface.

Main Results:

  • A functional tool enabling non-technical users to conduct NER on biomedical text.
  • Positive feedback from early adopters regarding ease of use and quick implementation.
  • Demonstrated utility as a starting point for incorporating NER into research.

Conclusions:

  • The developed tool simplifies NER for biomedical researchers without technical expertise.
  • The spreadsheet-based interface lowers the barrier to entry for utilizing advanced text analysis.
  • This system facilitates the integration of NER into diverse biomedical research workflows.