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Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods.

Buzhou Tang1, Qingcai Chen2, Xiaolong Wang2

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Summary
This summary is machine-generated.

This study introduces novel methods for recognizing disjoint clinical concepts (DCCs) in clinical text, improving upon existing systems that only handle consecutive concepts. The new approach significantly enhances the accuracy of clinical concept recognition (CCR).

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Area of Science:

  • Natural Language Processing (NLP)
  • Clinical Informatics
  • Machine Learning

Background:

  • Clinical concept recognition (CCR) is crucial for clinical NLP.
  • Current systems primarily identify consecutive clinical concepts (CCCs).
  • Disjoint clinical concepts (DCCs) are prevalent but often overlooked.

Purpose of the Study:

  • To develop novel representations for DCCs.
  • To apply state-of-the-art machine learning methods for recognizing both CCCs and DCCs.
  • To improve overall CCR performance.

Main Methods:

  • Proposed two novel representation types for DCCs.
  • Applied two advanced machine learning techniques.
  • Utilized the 2013 ShARe/CLEF challenge corpus for experiments.

Main Results:

  • Achieved a strict F-measure of 0.803 for CCCs.
  • Achieved a strict F-measure of 0.477 for DCCs.
  • Achieved a strict F-measure of 0.783 for all clinical concepts, outperforming baselines.

Conclusions:

  • The proposed methods effectively recognize DCCs, addressing a key limitation in current CCR systems.
  • This advancement significantly improves the comprehensive recognition of clinical concepts.
  • The findings offer a more robust approach to clinical NLP tasks.