Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings

TL;DR

This paper introduces a Transformer-based model with diverse label embeddings for automated ICD code assignment, improving accuracy and calibration over previous methods on the MIMIC-III dataset.

Contribution

It presents a novel approach combining Transformer models with label embeddings to enhance ICD coding accuracy and calibration, handling lengthy clinical narratives effectively.

Findings

Outperforms state-of-the-art models on MIMIC-III dataset

Uses label embeddings to improve code prediction accuracy

Achieves well-calibrated classification results

Abstract

Although the International Classification of Diseases (ICD) has been adopted worldwide, manually assigning ICD codes to clinical text is time-consuming, error-prone, and expensive, motivating the development of automated approaches. This paper describes a novel approach for automated ICD coding, combining several ideas from previous related work. We specifically employ a strong Transformer-based model as a text encoder and, to handle lengthy clinical narratives, we explored either (a) adapting the base encoder model into a Longformer, or (b) dividing the text into chunks and processing each chunk independently. The representations produced by the encoder are combined with a label embedding mechanism that explores diverse ICD code synonyms. Experiments with different splits of the MIMIC-III dataset show that the proposed approach outperforms the current state-of-the-art models in ICD coding, with the label embeddings significantly contributing to the good performance. Our approach also leads to properly calibrated classification results, which can effectively inform downstream tasks such as quantification.