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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.wnut-1.8.pdf