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Enhanced functionalities for annotating and indexing clinical text with the NCBO Annotator.

Andon Tchechmedjiev1, Amine Abdaoui1, Vincent Emonet1

  • 1Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier & CNRS, Montpellier 34090, France.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

NCBO Annotator+ enhances clinical text annotation by adding features like context detection and concept recognition. This proxy architecture extends the original service for better biomedical data analysis.

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Clinical data analysis

Background:

  • The National Center for Biomedical Ontology (NCBO) Annotator is widely used for biomedical text but is not ideal for clinical data.
  • Clinical text annotation requires specialized functionalities beyond standard biomedical data processing.

Purpose of the Study:

  • To enhance the NCBO Annotator for clinical text annotation without altering the original service.
  • To introduce new functionalities for improved text analysis and concept recognition in clinical data.

Main Methods:

  • Designed a proxy architecture for seamless extension of the NCBO Annotator.
  • Implemented pre-processing and post-processing steps for input text and annotations.
  • Developed enhanced functionalities including scoring, context detection (negation, experiencer, temporality), new output formats, and coarse-grained concept recognition using UMLS Semantic Groups.

Main Results:

  • Introduced NCBO Annotator+, a web service with enhanced clinical text annotation capabilities.
  • Evaluated concept recognition and clinical context detection on standard tasks (Clef eHealth 2017, SemEval 2014).
  • Successfully integrated Annotator+ into the SIFR BioPortal platform.

Conclusions:

  • The NCBO Annotator+ provides a flexible and extensible solution for clinical text annotation.
  • The proxy architecture enables in-house deployment for processing sensitive clinical data.
  • Open availability of code and Docker packaging facilitates wider adoption and use.