@inproceedings{feucht-etal-2021-description,
title = "Description-based Label Attention Classifier for Explainable {ICD}-9 Classification",
author = "Feucht, Malte and
Wu, Zhiliang and
Althammer, Sophia and
Tresp, Volker",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wnut-1.8/",
doi = "10.18653/v1/2021.wnut-1.8",
pages = "62--66",
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."
}
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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.</abstract>
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%0 Conference Proceedings
%T Description-based Label Attention Classifier for Explainable ICD-9 Classification
%A Feucht, Malte
%A Wu, Zhiliang
%A Althammer, Sophia
%A Tresp, Volker
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F feucht-etal-2021-description
%X 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.
%R 10.18653/v1/2021.wnut-1.8
%U https://aclanthology.org/2021.wnut-1.8/
%U https://doi.org/10.18653/v1/2021.wnut-1.8
%P 62-66
Markdown (Informal)
[Description-based Label Attention Classifier for Explainable ICD-9 Classification](https://aclanthology.org/2021.wnut-1.8/) (Feucht et al., WNUT 2021)
ACL