  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.
