Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification

TL;DR

This paper presents a novel multi-label medical document classification method that integrates deep text encoding, auxiliary medical knowledge, and ICD code co-occurrence patterns to improve automatic ICD coding accuracy.

Contribution

It introduces a multi-level deep dilated residual convolution encoder combined with auxiliary knowledge and graph convolutional networks for enhanced ICD code prediction.

Findings

Achieves state-of-the-art performance on ICD coding tasks.

Effectively leverages auxiliary medical knowledge and code co-occurrence.

Improves classification accuracy over existing methods.

Abstract

The International Classification of Diseases (ICD) is an authoritative medical classification system of different diseases and conditions for clinical and management purposes. ICD indexing assigns a subset of ICD codes to a medical record. Since human coding is labour-intensive and error-prone, many studies employ machine learning to automate the coding process. ICD coding is a challenging task, as it needs to assign multiple codes to each medical document from an extremely large hierarchically organized collection. In this paper, we propose a novel approach for ICD indexing that adopts three ideas: (1) we use a multi-level deep dilated residual convolution encoder to aggregate the information from the clinical notes and learn document representations across different lengths of the texts; (2) we formalize the task of ICD classification with auxiliary knowledge of the medical records, which incorporates not only the clinical texts but also different clinical code terminologies and drug prescriptions for better inferring the ICD codes; and (3) we introduce a graph convolutional network to leverage the co-occurrence patterns among ICD codes, aiming to enhance the quality of label representations. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures.