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Directional terms are essential for describing the relative locations of different body structures. For instance, an anatomist might describe one band of tissue as "inferior to" another, or a physician might describe a tumor as "superficial to" a deeper body structure. These terms often use comparative terms in pairs to trace out the relative locations of one body part to another or descriptions of body tissues like the deeper ones from superficially present with reference to...
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Clinical Abbreviation Disambiguation Using Deep Contextualized Representation.

Mingkai Peng1, Hude Quan1

  • 1Department of Community Health Sciences, University of Calgary, Calgary, Canada.

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Summary
This summary is machine-generated.

This study introduces a novel method for clinical abbreviation disambiguation, achieving 95% accuracy. It uses deep learning and cluster analysis to accurately identify the meaning of medical abbreviations.

Keywords:
clinical abbreviationscluster analysisdeep contextualized representation

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Area of Science:

  • Computational linguistics
  • Medical informatics
  • Natural Language Processing

Background:

  • Clinical abbreviations are often ambiguous, leading to potential misinterpretations in medical records.
  • Existing methods for abbreviation disambiguation may lack accuracy or efficiency.

Purpose of the Study:

  • To develop and evaluate a method for clinical abbreviation disambiguation.
  • To leverage deep contextualized representations and cluster analysis for improved accuracy.

Main Methods:

  • Utilized the pre-trained BioELMo language model for generating contextualized word vectors of abbreviations.
  • Applied Principal Component Analysis (PCA) for dimensionality reduction of word vectors.
  • Employed K-Means cluster analysis to group abbreviation senses.
  • Assigned cluster senses via majority vote of annotations.

Main Results:

  • Achieved an average accuracy of approximately 95% across 74 clinical abbreviations.
  • Demonstrated that each cluster requires approximately 5 annotated samples to determine its sense.

Conclusions:

  • The developed method effectively disambiguates clinical abbreviations with high accuracy.
  • The approach shows promise for enhancing the clarity and reliability of clinical documentation.