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Development of message passing-based graph convolutional networks for classifying cancer pathology reports.

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Summary

A new Fast Message Passing Network (FastMPN) improves clinical text classification by efficiently extracting tumor information from pathology reports. This graph convolutional network model offers competitive performance and faster training times compared to existing methods.

Keywords:
Cancer pathology reportsDeep learningGraphGraph convolutional networksGraph of wordsInformation extractionMessage passing networksNatural language processing

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Bioinformatics

Background:

  • Graph convolutional networks (GCNs) like TextGCN are effective for natural language text classification.
  • Existing TextGCN models face challenges with memory consumption and distribution.
  • A novel Fast Message Passing Network (FastMPN) is proposed to address these limitations.

Purpose of the Study:

  • To develop a more efficient and flexible GCN model for text classification.
  • To apply the FastMPN model to clinical information extraction from cancer pathology reports.
  • To extract key tumor properties including site, subsite, laterality, histology, behavior, and grade.

Main Methods:

  • Implemented a GCN with a message passing architecture (FastMPN).
  • Incorporated trainable node embedding and edge weights for enhanced model flexibility.
  • Applied the FastMPN to a dataset of cancer pathology reports for information extraction.

Main Results:

  • The FastMPN model demonstrated performance equivalent to or better than the multi-task convolutional neural network (MT-CNN) model.
  • Evaluated using micro- and macro-averaged F1 scores for clinical task performance.
  • Achieved highly competitive clinical task performance scores for tumor information extraction.

Conclusions:

  • The FastMPN model, implemented on PyTorch, efficiently trains large corpora (667,290 samples) in under 3 minutes per epoch on a V100 GPU.
  • The FastMPN offers a versatile and flexible approach to GCN-based text classification.
  • This implementation provides a competitive solution for extracting critical tumor information from pathology reports.