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

Malte Feucht, Zhiliang Wu, Sophia Althammer, Volker Tresp


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.
Anthology ID:
2021.wnut-1.8
Volume:
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
Month:
November
Year:
2021
Address:
Online
Editors:
Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
62–66
Language:
URL:
https://aclanthology.org/2021.wnut-1.8
DOI:
10.18653/v1/2021.wnut-1.8
Bibkey:
Cite (ACL):
Malte Feucht, Zhiliang Wu, Sophia Althammer, and Volker Tresp. 2021. Description-based Label Attention Classifier for Explainable ICD-9 Classification. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 62–66, Online. Association for Computational Linguistics.
Cite (Informal):
Description-based Label Attention Classifier for Explainable ICD-9 Classification (Feucht et al., WNUT 2021)
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PDF:
https://aclanthology.org/2021.wnut-1.8.pdf