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ICD-9 coding is a relevant clinical billing task, where unstructured textswith information about a patient's diagnosis and treatments are annotated withmultiple ICD-9 codes. Automated ICD-9 coding is an active research field, whereCNN- and RNN-based model architectures represent the state-of-the-artapproaches. In this work, we propose a description-based label attentionclassifier to improve the model explainability when dealing with noisy textslike clinical notes. We evaluate our proposed method with differenttransformer-based encoders on the MIMIC-III-50 dataset. Our method achievesstrong results together with augmented explainablilty.

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