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Topic Modeling Based Classification of Clinical Reports.

Efsun Sarioglu1, Kabir Yadav2, Hyeong-Ah Choi1

  • 1Computer Science Department, The George Washington University, Washington, DC, USA.

Proceedings of the Conference. Association for Computational Linguistics. Meeting
|October 3, 2023
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Summary
This summary is machine-generated.

This study introduces topic modeling for classifying electronic health records (EHRs) text. Topic-based classification offers a faster, more interpretable alternative to traditional methods for clinical data analysis.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Computational Health

Background:

  • Electronic health records (EHRs) contain valuable clinical data, often in unstructured free text.
  • Preprocessing EHR free text is crucial for its use in automated healthcare systems.
  • Efficient data utilization can significantly improve healthcare quality and speed.

Purpose of the Study:

  • To analyze the classification of CT imaging reports using topic modeling techniques.
  • To explore topic modeling as a method for efficient and interpretable EHR data representation and classification.
  • To compare topic-based classification with existing text classification methods.

Main Methods:

  • Applied topic modeling to a dataset of CT imaging reports.
  • Utilized regular text classification alongside various topic modeling approaches.
  • Developed a binary topic model for unsupervised classification.
  • Created an aggregate topic classifier based on discriminative topics.

Main Results:

  • Topic modeling provided interpretable themes within the clinical reports.
  • Topic distributions offered a more compact and faster representation than bag-of-words.
  • The binary topic model served as an unsupervised classification strategy.
  • The aggregate topic classifier achieved competitive performance with existing techniques.

Conclusions:

  • Topic-based classification of EHRs is a viable and competitive approach.
  • This method offers enhanced efficiency and interpretability for clinical text analysis.
  • Topic modeling can streamline the processing of unstructured EHR data for automated systems.