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

• URL: http://arxiv.org/abs/2109.12026v1
• Date: Fri, 24 Sep 2021 15:31:38 GMT
• Title: Description-based Label Attention Classifier for Explainable ICD-9 Classification
• Authors: Malte Feucht, Zhiliang Wu, Sophia Althammer, Volker Tresp
• Abstract summary: 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.
• Score: 10.407041139832955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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