Description-based Label Attention Classifier for Explainable ICD-9 Classification

Abstract

ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient's diagnosis and treatments are annotated with multiple ICD-9 codes. Automated ICD-9 coding is an active research field, where CNN- and RNN-based model architectures represent the state-of-the-art approaches. In this work, we propose a description-based label attention classifier to improve the model explainability when dealing with noisy texts like clinical notes. We evaluate our proposed method with different transformer-based encoders on the MIMIC-III-50 dataset. Our method achieves strong results together with augmented explainablilty.

arXiv commentAccepted at the Workshop on Noisy User-generated Text (W-NUT) at EMNLP 2021

References in corpus (5)

• Distributed Representations of Words and Phrases and their Compositionality
• Longformer: The Long-Document Transformer
• Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
• A Label Attention Model for ICD Coding from Clinical Text
• Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study