Multitask Balanced and Recalibrated Network for Medical Code Prediction

Published in

ACM Transactions on Intelligent Systems and Tec...(2021)

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TL;DR

A novel neural network, called the Multitask Balanced and Recalibrated Neural Network, is proposed to solve the imbalanced class problem and outperforms competitive baselines on a real-world clinical dataset called the Medical Information Mart for Intensive Care (MIMIC-III).

Abstract

Human coders assign standardized medical codes to clinical documents generated during patients’ hospitalization, which is error prone and labor intensive. Automated medical coding approaches have been developed using machine learning methods, such as deep neural networks. Nevertheless, automated medical coding is still challenging because of complex code association, noise in lengthy documents, and the imbalanced class problem. We propose a novel neural network, called the Multitask Balanced and Recalibrated Neural Network, to solve these issues. Significantly, the multitask learning scheme shares the relationship knowledge between different coding branches to capture code association. A recalibrated aggregation module is developed by cascading convolutional blocks to extract high-level semantic features that mitigate the impact of noise in documents. Also, the cascaded structure of the recalibrated module can benefit learning from lengthy notes. To solve the imbalanced class problem, we deploy focal loss to redistribute the attention on low- and high-frequency medical codes. Experimental results show that our proposed model outperforms competitive baselines on a real-world clinical dataset called the Medical Information Mart for Intensive Care (MIMIC-III).

Authors

E. Cambria

25 papers

Shaoxiong Ji

7 papers

Wei Sun

2 papers

References51 items

Hierarchical BERT for Medical Document Understanding

Leveraging data and information systems on the sustainable development goals

FNet: Mixing Tokens with Fourier Transforms

Multitask Recalibrated Aggregation Network for Medical Code Prediction

Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study

Multitask Balanced and Recalibrated Network for Medical Code Prediction

Published in

ACM Transactions on Intelligent Systems and Tec...(2021)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

E. Cambria

25 papers

Shaoxiong Ji

7 papers

Wei Sun

2 papers

References51 items

Hierarchical BERT for Medical Document Understanding

Leveraging data and information systems on the sustainable development goals

FNet: Mixing Tokens with Fourier Transforms

Multitask Recalibrated Aggregation Network for Medical Code Prediction

Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study

Pekka Marttinen

2 papers

TL;DR

Abstract

Authors

References51 items

Hierarchical BERT for Medical Document Understanding

Leveraging data and information systems on the sustainable development goals

FNet: Mixing Tokens with Fourier Transforms

Multitask Recalibrated Aggregation Network for Medical Code Prediction

Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study

TL;DR

Abstract

Authors

References51 items

Hierarchical BERT for Medical Document Understanding

Leveraging data and information systems on the sustainable development goals

FNet: Mixing Tokens with Fourier Transforms

Multitask Recalibrated Aggregation Network for Medical Code Prediction

Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study

Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation

Medical Code Assignment with Gated Convolution and Note-Code Interaction

Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text

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Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

SPECIAL ISSUE ON LEARNING FROM IMBALANCED DATA SETS

Mixture of Expert Agents for Handling Imbalanced Data Sets

SMOTE: Synthetic Minority Over-sampling Technique

Combining Classiiers in Text Categorization

[ Received 10 April 2022; revised 12 August 2022; accepted 30 August 2022

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