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Enhancing medical named entity recognition with an extended segment representation technique.

Sara Keretna1, Chee Peng Lim1, Doug Creighton1

  • 1Centre for Intelligent Systems Research, Deakin University, Australia.

Computer Methods and Programs in Biomedicine
|March 21, 2015
PubMed
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This study introduces an improved segment representation (SR) method to boost named entity recognition (NER) accuracy in medical texts. The enhanced technique effectively handles ambiguous terms, leading to better identification of medical entities.

Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Machine Learning

Background:

  • Named Entity Recognition (NER) is crucial for extracting information from clinical text.
  • Existing segment representation (SR) techniques, like IOBES, face challenges with context-dependent entities.
  • Ambiguity in word classification can degrade the performance of NER systems.

Purpose of the Study:

  • To develop an extended segment representation (SR) technique for improved medical NER.
  • To address the ambiguity of words that function as named entities (NEs) in some contexts but not others.
  • To enhance the accuracy of classification-based NER models in the medical domain.

Main Methods:

  • An extension to the Inside/Outside/Begin/End/Single (IOBES) SR technique was formulated.
Keywords:
Biomedical text annotationBiomedical text miningInformation extractionNatural language processingUnstructured electronic medical records

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  • A novel class was introduced for words exhibiting context-dependent NE status.
  • This approach aims to reduce ambiguity and improve classifier performance.

    • Main Results:

    • The extended SR technique was evaluated on the i2b2 2010 medical challenge dataset.
    • Eight different classifiers were used to extract medical NEs: treatment, problem, and test.
    • Seven out of eight classifiers showed improved average F1-measure results, with C4.5 achieving a 9.33% average improvement.

    Conclusions:

    • The proposed extended SR technique significantly enhances NER performance in medical applications.
    • Accurate identification of medical entities like treatments, problems, and tests is improved.
    • This method offers a valuable advancement for clinical text analysis and information extraction.