Multitask Recalibrated Aggregation Network for Medical Code Prediction

• URL: http://arxiv.org/abs/2104.00952v1
• Date: Fri, 2 Apr 2021 09:22:10 GMT
• Title: Multitask Recalibrated Aggregation Network for Medical Code Prediction
• Authors: Wei Sun and Shaoxiong Ji and Erik Cambria and Pekka Marttinen
• Abstract summary: We propose a multitask recalibrated aggregation network to solve the challenges of encoding lengthy and noisy clinical documents. In particular, multitask learning shares information across different coding schemes and captures the dependencies between different medical codes. Experiments with a real-world MIMIC-III dataset show significantly improved predictive performance.
• Score: 19.330911490203317
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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