Multitask Recalibrated Aggregation Network for Medical Code Prediction

Published in

(2021)

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TL;DR

A multitask recalibrated aggregation network that shares information across different coding schemes and captures the dependencies between different medical codes is proposed to solve the challenges of encoding lengthy and noisy clinical documents and capturing code associations.

Abstract

Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement. Manual coding by trained human coders is time-consuming and error-prone. Thus, automated coding algorithms have been developed, building especially on the recent advances in machine learning and deep neural networks. To solve the challenges of encoding lengthy and noisy clinical documents and capturing code associations, we propose a multitask recalibrated aggregation network. In particular, multitask learning shares information across different coding schemes and captures the dependencies between different medical codes. Feature recalibration and aggregation in shared modules enhance representation learning for lengthy notes. Experiments with a real-world MIMIC-III dataset show significantly improved predictive performance.

Authors

E. Cambria

25 papers

Shaoxiong Ji

7 papers

Wei Sun

2 papers

References36 items

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

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HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding

ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

Multitask Recalibrated Aggregation Network for Medical Code Prediction

Published in

(2021)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

E. Cambria

25 papers

Shaoxiong Ji

7 papers

Wei Sun

2 papers

References36 items

Leveraging data and information systems on the sustainable development goals

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

Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text

HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding

ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

Pekka Marttinen

2 papers

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Field of Study

Computer Science