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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Automatic International Classification of Diseases Coding via Note-Code Interaction Network with Denoising Mechanism.

Xiaobo Li1, Yijia Zhang1, Xingwang Li1

  • 1School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|August 11, 2023
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Summary

The Note-code Interaction Denoising Network (NIDN) improves automatic medical coding by using self-attention to extract key information from electronic medical records (EMRs) and reduce noise. This deep learning approach enhances accuracy in assigning International Classification of Diseases (ICD) codes.

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attention mechanismautomatic ICD codingdenoising modulemultitask learning

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Clinical Documentation

Background:

  • Accurate medical coding from clinical notes is challenging due to data complexity, large volumes, and noise.
  • Deep learning shows promise for automatic International Classification of Diseases (ICD) coding but faces issues like class imbalance and code association complexity.
  • Existing methods struggle with noisy records and imbalanced code distribution, limiting accuracy in electronic medical record (EMR) analysis.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate automatic medical coding.
  • To address challenges in ICD coding, including data noise, class imbalance, and complex code relationships.
  • To improve the extraction of semantic features and code-specific expressions from clinical notes.

Main Methods:

  • Introduced the Note-code Interaction Denoising Network (NIDN) utilizing self-attention mechanisms for feature extraction from EMRs.
  • Employed a label attention mechanism to retain code-specific textual information.
  • Integrated Clinical Classifications Software coding for multitask learning and incorporated a denoising module to mitigate noise and imbalance.

Main Results:

  • The NIDN model demonstrated superior performance compared to existing models on the Medical Information Mart for Intensive Care (MIMIC-III) dataset.
  • The self-attention and label attention mechanisms effectively captured critical semantic features and code-specific expressions.
  • The denoising module successfully reduced the impact of noise and improved handling of label distribution imbalance.

Conclusions:

  • The NIDN model represents a significant advancement in deep learning for automatic ICD coding.
  • The proposed methods effectively address key challenges in processing complex and noisy clinical data.
  • NIDN offers a robust solution for enhancing the accuracy and efficiency of medical coding from EMRs.