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A Pseudo Label-Wise Attention Network for Automatic ICD Coding.

Yifan Wu, Min Zeng, Ying Yu

    IEEE Journal of Biomedical and Health Informatics
    |July 22, 2022
    PubMed
    Summary
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    We introduce a pseudo label-wise attention mechanism for automatic International Classification of Diseases (ICD) coding. This method efficiently merges similar ICD codes, reducing computational costs and improving accuracy for electronic medical record analysis.

    Area of Science:

    • Computational medicine and health informatics.

    Background:

    • Automatic International Classification of Diseases (ICD) coding is a challenging multi-label text classification task due to a vast number of unbalanced labels.
    • Existing label-wise attention mechanisms in Electronic Medical Records (EMR) analysis are computationally expensive and redundant.

    Purpose of the Study:

    • To propose a novel pseudo label-wise attention mechanism to enhance the efficiency and accuracy of automatic ICD coding.
    • To develop a method capable of predicting new ICD codes by leveraging EMR and ICD vector similarities.

    Main Methods:

    • Developed a pseudo label-wise attention mechanism that merges similar ICD codes, computing a single attention mode for each group.
    • Implemented a novel approach for obtaining ICD vectors to facilitate prediction of new ICD codes.
    • Validated the model on the MIMIC-III and Xiangya datasets.

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    Main Results:

    • Achieved state-of-the-art performance on micro F1 and micro AUC metrics on both datasets.
    • Significantly reduced GPU memory usage by approximately 73.9% compared to traditional label-wise attention models.
    • Demonstrated the model's capability in predicting new ICD codes, supported by interpretability analysis and case studies.

    Conclusions:

    • The pseudo label-wise attention mechanism offers a computationally efficient and accurate solution for automatic ICD coding.
    • The proposed method effectively compresses attention modes, leading to improved predictive accuracy and reduced resource consumption.
    • The model's ability to predict new ICD codes enhances its utility in clinical practice and medical research.