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Deformable phrase level attention: A flexible approach for improving AI based medical coding.

Christoph Metzner1, Shang Gao2, Drahomira Herrmannova3

  • 1The Bredesen Center, The University of Tennessee, Knoxville, TN 37996, USA; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

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Summary

This study introduces a new deformable, phrase-level attention mechanism to improve AI-driven medical encoding of clinical text. The novel method enhances text classification models, leading to better extraction of medical concepts and improved population health insights.

Keywords:
Automated medical encodingCommon data modelsElectronic health recordsMachine learningNatural language processing

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Area of Science:

  • Artificial Intelligence in Medicine
  • Natural Language Processing for Healthcare
  • Clinical Informatics

Background:

  • Automated medical encoding of clinical text is crucial for population health surveillance.
  • Current AI models require enhancement for accurate medical concept classification in electronic health records.

Purpose of the Study:

  • To present a novel deformable, phrase-level attention mechanism for enhancing AI-driven text classification models.
  • To improve the extraction of medical concepts from unstructured clinical text.

Main Methods:

  • Developed a deformable, phrase-level attention mechanism to capture word-level and phrase-level information.
  • Evaluated deep learning models (conventional and transformer-based) augmented with the attention mechanism.
  • Tested on extracting cancer information from pathology reports and medical encoding of hospital discharge summaries.

Main Results:

  • Transformer-based models with the novel attention mechanism achieved superior performance in cancer information extraction.
  • Both model types showed comparable or improved performance in automated medical encoding tasks.
  • Phrase-level attention outperformed standard word-level attention.

Conclusions:

  • The proposed deformable, phrase-level attention mechanism enhances medical concept extraction from clinical text.
  • The method demonstrates robustness and suitability for real-world applications like automated data harmonization.
  • This approach shows promise for improving automated data harmonization for common data models.