Description-based Label Attention Classifier for Explainable ICD-9 Classification
Malte Feucht, Zhiliang Wu, Sophia Althammer, Volker Tresp · 2021
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.