Revisiting neural relation classification in clinical notes with external information

Simon Šuster, Madhumita Sushil, Walter Daelemans


Abstract
Recently, segment convolutional neural networks have been proposed for end-to-end relation extraction in the clinical domain, achieving results comparable to or outperforming the approaches with heavy manual feature engineering. In this paper, we analyze the errors made by the neural classifier based on confusion matrices, and then investigate three simple extensions to overcome its limitations. We find that including ontological association between drugs and problems, and data-induced association between medical concepts does not reliably improve the performance, but that large gains are obtained by the incorporation of semantic classes to capture relation triggers.
Anthology ID:
W18-5603
Volume:
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | Louhi | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
22–28
Language:
URL:
https://aclanthology.org/W18-5603
DOI:
10.18653/v1/W18-5603
Bibkey:
Cite (ACL):
Simon Šuster, Madhumita Sushil, and Walter Daelemans. 2018. Revisiting neural relation classification in clinical notes with external information. In Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, pages 22–28, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Revisiting neural relation classification in clinical notes with external information (Šuster et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5603.pdf
Code
 SimonSuster/seg_cnn