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arXiv · 2109.12026

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

Malte Feucht·Zhiliang Wu·Sophia Althammer·Volker Tresp
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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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Malte Feucht, Zhiliang Wu, Sophia Althammer, Volker Tresp. 2021-09-24. Description-based Label Attention Classifier for Explainable ICD-9 Classification. https://arxiv.org/abs/2109.12026

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