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Clinical report classification using Natural Language Processing and Topic Modeling.

Efsun Sarioglu1, Hyeong-Ah Choi1, Kabir Yadav2

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

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|September 28, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

This study enhances the classification of Emergency Department computed tomography (CT) reports using Natural Language Processing (NLP) and topic modeling. NLP with modifier filtering significantly improved injury coding accuracy for trauma patients.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Electronic health records contain vast amounts of unstructured clinical text.
  • Efficiently processing and coding this text is crucial for medical decision-making.
  • Accurate classification of Emergency Department (ED) computed tomography (CT) reports is needed.

Purpose of the Study:

  • To investigate techniques for improving the classification of ED CT reports.
  • To develop a system using NLP and machine learning for coding clinically important injuries in traumatic orbital fracture victims.
  • To explore topic modeling as an alternative text representation for classification.

Main Methods:

  • Utilized Natural Language Processing (NLP) to extract structured information from free-text CT reports.
  • Applied machine learning techniques for classifying the presence of injuries.
  • Employed topic modeling to represent patient reports in a lower-dimensional space.
  • Investigated NLP feature filtering using modifiers and binary topic classification.

    • Main Results:

    • Both NLP and topic modeling approaches improved raw text classification performance.
    • Filtering NLP codes with modifiers yielded the best classification results.
    • Topic modeling provided effective dimensionality reduction with comparable classification accuracy to NLP features.
    • Binary topic classification did not outperform raw text classification.

    Conclusions:

    • NLP techniques, particularly with modifier filtering, are effective for enhancing CT report classification.
    • Topic modeling offers a viable alternative for dimensionality reduction and classification of clinical text.
    • Further research may optimize topic modeling for improved clinical data analysis.