Multitask Recalibrated Aggregation Network for Medical Code Prediction

TL;DR

This paper introduces a multitask recalibrated aggregation network that improves automated medical code prediction by effectively handling lengthy, noisy clinical documents and capturing code dependencies, leading to better performance.

Contribution

It proposes a novel multitask deep learning model with feature recalibration for medical coding, addressing challenges of document length and code dependency modeling.

Findings

Significant performance improvement on MIMIC-III dataset

Effective handling of lengthy and noisy clinical notes

Enhanced capture of code dependencies

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.