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Using case-level context to classify cancer pathology reports.

Shang Gao1, Mohammed Alawad1, Noah Schaefferkoetter1

  • 1Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States of America.

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This study introduces a new method to improve cancer report analysis by using case-level context from multiple electronic health records (EHRs). This approach enhances classification accuracy for key cancer characteristics.

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Area of Science:

  • Medical Informatics
  • Computational Pathology
  • Artificial Intelligence in Healthcare

Background:

  • Individual electronic health records (EHRs) and clinical reports are often sequential, requiring analysis of aggregate case-level data.
  • Extracting comprehensive cancer case information necessitates integrating data from multiple reports over a disease's trajectory.

Purpose of the Study:

  • To develop a modular add-on for deep learning models to capture case-level context from sequential clinical reports.
  • To enhance the accuracy of text classification tasks on cancer pathology reports by incorporating holistic case information.

Main Methods:

  • A modular add-on was developed for compatibility with existing deep learning text classification architectures.
  • The approach was evaluated on a large corpus of 431,433 cancer pathology reports.
  • Classification accuracy was assessed across six key tasks: site, subsite, laterality, histology, behavior, and grade.

Main Results:

  • Incorporating case-level context significantly improved classification accuracy across all six evaluated tasks.
  • The proposed add-on demonstrated a substantial boost in performance for analyzing cancer pathology reports.
  • The method proved effective in capturing aggregate information from sequential EHR data.

Conclusions:

  • The developed add-on effectively captures case-level context, significantly enhancing classification accuracy in cancer pathology reports.
  • This modular approach is adaptable to various deep learning architectures and clinical text-based tasks.
  • Integrating sequential report data improves the understanding and analysis of complex diseases like cancer.