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

Malte Feucht, Zhiliang Wu, Sophia Althammer, Volker Tresp

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.