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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Medical Specialty Classification Based on Semiadversarial Data Augmentation.

Huan Zhang1,2, Dong Zhu1, Hao Tan1,2

  • 1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China.

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|October 26, 2023
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Summary
This summary is machine-generated.

This study introduces a novel data augmentation technique using adversarial attacks to improve automated medical specialty classification from electronic health records (EHRs). The method enhances accuracy and F1 score on imbalanced datasets, aiding clinical practice.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Electronic health record (EHR) adoption necessitates automated medical specialty classification for efficient data retrieval and clinical decision support.
  • Current challenges include imbalanced and insufficient medical note data, multicategory classification complexity, and difficulties in de-identifying sensitive information.

Purpose of the Study:

  • To propose a novel data augmentation method using adversarial attacks to address data limitations in medical specialty classification.
  • To design a classification framework that incorporates probabilistic noun information and confidence recalculation for improved accuracy.

Main Methods:

  • A data augmentation method based on adversarial attacks, generating semi-adversarial examples to expand training data coverage.
  • A classification framework integrating probabilistic noun information with confidence recalculation post-softmax layer.
  • Validation on an 18-class, highly imbalanced dataset.

Main Results:

  • The proposed method significantly improved accuracy and F1 score compared to four benchmark methods.
  • An average improvement of 14.9% in accuracy and F1 score was observed.
  • The technique effectively expanded the training data's decision space coverage.

Conclusions:

  • The adversarial attack-based data augmentation and probabilistic noun-focused classification framework offer a robust solution for imbalanced medical specialty classification.
  • This approach enhances the efficiency and reliability of EHR data analysis for clinical applications.