Paper Info
Title
Learning to Infer Entities, Properties and their Relations from Clinical Conversations
Abstract
Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019) 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.
Year
DOI
Venue
2019
10.18653/v1/D19-1503
EMNLP/IJCNLP (1)
DocType
Volume
ISSN
Conference
D19-1
Proc. Empirical Methods in Natural Language Processing, 2019
Citations 
PageRank 
References 
1
0.37
0
Authors
5
Name
Order
Citations
PageRank
Nan Du150352.49
Mingqiu Wang210.37
Linh Tran310.71
Gang Lee410.37
Izhak Shafran530030.44