Learning to Infer Entities, Properties and their Relations from Clinical Conversations

Nan Du, Mingqiu Wang, Linh Tran, Gang Lee, Izhak Shafran


Abstract
Recently we proposed the Span Attribute Tagging (SAT) Model to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). It tackles the challenge of large label space and limited training data using a hierarchical two-stage approach that identifies the span of interest in a tagging step and assigns labels to the span in a classification step. We extend the SAT model to jointly infer not only entities and their properties but also relations between them. Most relation extraction models restrict inferring relations between tokens within a few neighboring sentences, mainly to avoid high computational complexity. In contrast, our proposed Relation-SAT (R-SAT) model is computationally efficient and can infer relations over the entire conversation, spanning an average duration of 10 minutes. We evaluate our model on a corpus of clinical conversations. When the entities are given, the R-SAT outperforms baselines in identifying relations between symptoms and their properties by about 32% (0.82 vs 0.62 F-score) and by about 50% (0.60 vs 0.41 F-score) on medications and their properties. On the more difficult task of jointly inferring entities and relations, the R-SAT model achieves a performance of 0.34 and 0.45 for symptoms and medications respectively, which is significantly better than 0.18 and 0.35 for the baseline model. The contributions of different components of the model are quantified using ablation analysis.
Anthology ID:
D19-1503
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4979–4990
Language:
URL:
https://aclanthology.org/D19-1503
DOI:
10.18653/v1/D19-1503
Bibkey:
Cite (ACL):
Nan Du, Mingqiu Wang, Linh Tran, Gang Lee, and Izhak Shafran. 2019. Learning to Infer Entities, Properties and their Relations from Clinical Conversations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4979–4990, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Learning to Infer Entities, Properties and their Relations from Clinical Conversations (Du et al., EMNLP-IJCNLP 2019)
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PDF:
https://aclanthology.org/D19-1503.pdf