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Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label

M Venkata Krishna Reddy1, L Raghavendar Raju2, Kashi Sai Prasad3

  • 1Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology (Autonomous), Gandipet, Hyderabad, India. krishnareddy_cse@cbit.ac.in.

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Summary

The Enhanced Effective Convolutional Attention Network (EECAN) improves clinical document classification using deep learning. This AI model enhances feature extraction for more accurate organization of medical information in Electronic Health Records.

Keywords:
Artificial intelligenceClinical document classificationConvolutional attention networkDeep learningSqueeze and excitation inception

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Deep Learning for Medical Informatics
  • Natural Language Processing in Clinical Settings

Background:

  • Clinical Document Classification (CDC) is vital for managing vast medical data, improving patient care, research, and administrative tasks.
  • Current deep learning models show promise but require enhanced accuracy for long clinical documents.
  • The need for advanced AI solutions to effectively categorize complex medical texts is growing.

Purpose of the Study:

  • To introduce the Enhanced Effective Convolutional Attention Network (EECAN) for improved automatic clinical document classification.
  • To enhance feature representation and extraction from clinical documents using novel deep learning strategies.
  • To address limitations in current methods for classifying lengthy and multi-label clinical texts.

Main Methods:

  • Proposed the EECAN model, integrating a Squeeze-and-Excitation (SE) Inception module for adaptive feature recalibration.
  • Introduced the Encoder and Attention-Based Clinical Document Classification (EAB-CDC) strategy within EECAN.
  • Utilized sum-pooling and multi-layer attention mechanisms for discriminative feature extraction from clinical text representations.

Main Results:

  • EECAN demonstrated superior performance compared to existing deep learning approaches on benchmark datasets (MIMIC-III, MIMIC-III-50).
  • Achieved high AUC scores of 99.70% with sum-pooling and 99.80% with multi-layer attention.
  • The model effectively transformed multi-label clinical texts' contexts without information loss.

Conclusions:

  • The EECAN model offers a significant advancement in automated clinical document classification.
  • Its high accuracy and efficiency suggest strong potential for integration into Electronic Health Record (EHR) systems.
  • This approach can enhance healthcare decision-making support through improved medical information organization.