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This study introduces Red Flag/Blue Flag (RFBF), an explainable AI tool for TextCNN models in clinical text classification. RFBF enhances model interpretability and helps prevent overfitting, potentially improving patient outcomes.

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CNNNLPX-AIclinical NLPexplainable AItext classification

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Medical Informatics

Background:

  • TextCNN models are widely used for clinical text classification.
  • Assessing model interpretability, especially for smaller datasets, remains a challenge.
  • Overfitting can lead to misclassification by activating irrelevant features.

Purpose of the Study:

  • Introduce Red Flag/Blue Flag (RFBF), a novel explainable AI (X-AI) software for TextCNN binary classification.
  • Visualize convolutional filter discriminative capabilities for enhanced model interpretability.
  • Provide tools for model diagnosis, feature verification, and overfit prevention.

Main Methods:

  • Developed RFBF software for TextCNN models.
  • Visualized convolutional filter discriminative capabilities.
  • Conducted experiments on physician-authored operative notes for surgical misadventure prediction.

Main Results:

  • RFBF offers a more informative approach than direct logit contribution assessment.
  • Demonstrated filter consistency assessment, predictive performance improvement, and information leakage estimation.
  • Utilized RFBF to analyze TextCNN performance in predicting surgical misadventure outcomes.

Conclusions:

  • RFBF enhances the interpretability of TextCNN models in clinical text analysis.
  • The X-AI approach aids in diagnosing, verifying, and preventing overfitting in medical text classification.
  • This tool can benefit clinical text research and contribute to improved patient outcomes.