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.
Yang Liu, Hua Cheng, Russell Klopfer, Matthew R. Gormley, and Thomas Schaaf. 2021.Effective Convolutional Attention Network for Multi-label Clinical Document Classification. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5941–5953, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
@inproceedings{liu-etal-2021-effective, title = "Effective Convolutional Attention Network for Multi-label Clinical Document Classification", author = "Liu, Yang and Cheng, Hua and Klopfer, Russell and Gormley, Matthew R. and Schaaf, Thomas", editor = "Moens, Marie-Francine and Huang, Xuanjing and Specia, Lucia and Yih, Scott Wen-tau", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.481/", doi = "10.18653/v1/2021.emnlp-main.481", pages = "5941--5953", 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."}
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%0 Conference Proceedings%T Effective Convolutional Attention Network for Multi-label Clinical Document Classification%A Liu, Yang%A Cheng, Hua%A Klopfer, Russell%A Gormley, Matthew R.%A Schaaf, Thomas%Y Moens, Marie-Francine%Y Huang, Xuanjing%Y Specia, Lucia%Y Yih, Scott Wen-tau%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing%D 2021%8 November%I Association for Computational Linguistics%C Online and Punta Cana, Dominican Republic%F liu-etal-2021-effective%X 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.%R 10.18653/v1/2021.emnlp-main.481%U https://aclanthology.org/2021.emnlp-main.481/%U https://doi.org/10.18653/v1/2021.emnlp-main.481%P 5941-5953
Yang Liu, Hua Cheng, Russell Klopfer, Matthew R. Gormley, and Thomas Schaaf. 2021.Effective Convolutional Attention Network for Multi-label Clinical Document Classification. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5941–5953, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.