Effective Convolutional Attention Network for Multi-label Clinical Document Classification
Yang Liu, Hua Cheng, Russell Klopfer, Matthew R. Gormley, Thomas Schaaf
Abstract
Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels. In this paper, we present an effective convolutional attention network for the MLDC problem with a focus on medical code prediction from clinical documents. Our innovations are three-fold: (1) we utilize a deep convolution-based encoder with the squeeze-and-excitation networks and residual networks to aggregate the information across the document and learn meaningful document representations that cover different ranges of texts; (2) we explore multi-layer and sum-pooling attention to extract the most informative features from these multi-scale representations; (3) we combine binary cross entropy loss and focal loss to improve performance for rare labels. We focus our evaluation study on MIMIC-III, a widely used dataset in the medical domain. Our models outperform prior work on medical coding and achieve new state-of-the-art results on multiple metrics. We also demonstrate the language independent nature of our approach by applying it to two non-English datasets. Our model outperforms prior best model and a multilingual Transformer model by a substantial margin.- Anthology ID:
- 2021.emnlp-main.481
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5941–5953
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2021.emnlp-main.481/
- DOI:
- 10.18653/v1/2021.emnlp-main.481
- Cite (ACL):
- Yang Liu, Hua Cheng, Russell Klopfer, Matthew R. Gormley, and Thomas Schaaf. 2021. Effective Convolutional Attention Network for Multi-label Clinical Document Classification. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5941–5953, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Effective Convolutional Attention Network for Multi-label Clinical Document Classification (Liu et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2021.emnlp-main.481.pdf
- Data
- MIMIC-III