MHLAT: Multi-hop Label-wise Attention Model for Automatic ICD Coding

TL;DR

This paper introduces MHLAT, a multi-hop label-wise attention model for automatic ICD coding that improves accuracy and efficiency by mimicking human reading strategies and reducing memory usage.

Contribution

The paper proposes a novel multi-hop label-wise attention mechanism that enhances ICD coding accuracy while using fewer parameters than existing pretrained models.

Findings

Achieves superior or competitive performance on benchmark datasets.

Uses significantly fewer parameters than pretrained language models.

Demonstrates effectiveness across multiple evaluation metrics.

Abstract

International Classification of Diseases (ICD) coding is the task of assigning ICD diagnosis codes to clinical notes. This can be challenging given the large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000 tokens). However, unlike the single-pass reading process in previous works, humans tend to read the text and label definitions again to get more confident answers. Moreover, although pretrained language models have been used to address these problems, they suffer from huge memory usage. To address the above problems, we propose a simple but effective model called the Multi-Hop Label-wise ATtention (MHLAT), in which multi-hop label-wise attention is deployed to get more precise and informative representations. Extensive experiments on three benchmark MIMIC datasets indicate that our method achieves significantly better or competitive performance on all seven metrics, with much fewer parameters to optimize.