Electronic medical records (EMRs) contain a variety of valuable medical concepts and relations. The ability to recognize relations between medical concepts described in EMRs enables the automatic processing of clinical texts, resulting in an improved quality of health-related data analysis. Driven by the 2010 i2b2/VA Challenge Evaluation, the relation recognition problem in EMRs has been studied by many researchers to address this important aspect of EMR information extraction. This paper proposes an Attention-Based Deep Residual Network (ResNet) model to recognize medical concept relations in Chinese EMRs. Our model achieves F1-score of 77.80% on the manually annotated Chinese EMRs corpus and outperforms the state-of-the-art approaches. The residual network-based model can reduce the negative impact of corpus noise to parameter learning, and the combination of character position attention mechanism will enhance the identification features of different type of entities.

Electronic medical records (EMRs) contain a variety of valuable medical entities and their relations. The extraction of medical entities and their relations has important application value in the structuring of EMR and the development of various types of intelligent assistant medical systems, and hence is a hot issue in intelligent medicine research. In recent years, most research aims to firstly identify entities and then to recognize the relations between the entities, and often suffers from many redundant operations. Furthermore, the challenge remains of identifying overlapping relation triplets along with the entire medical entity boundary and detecting multi-type relations. In this work, we propose a Specific Relation Attention-guided Graph Neural Networks (SRAGNNs) model to jointly extract entities and their relations in Chinese EMR, which uses sentence information and attention-guided graph neural networks to perceive the features of every relation in a sentence and then to extract those relations. In addition, a specific sentence representation is constructed for every relation, and sequence labeling is performed to extract its corresponding head and tail entities. Experiments on a medical evaluation dataset and a manually labeled Chinese EMR dataset show that our model improves the performance of Chinese medical entities and relation extraction.

A spoken language understanding (SLU) system usually involves two subtasks: intent detection (ID) and slot filling (SF). Recently, joint modeling of ID and SF has been empirically demonstrated to lead to improved performance. However, the existing joint models cannot explicitly use the encoded information of the two subtasks to realize mutual interaction, nor can they achieve the bidirectional connection between them. In this paper, we propose a typed abstraction mechanism to enhance the performance of intent detection by utilizing the encoded information of SF tasks. In addition, we design a typed iteration approach, which can achieve the bidirectional connection of the encoded information and mitigate the negative effects of error propagation. The experimental results on two public datasets ATIS and SNIPS present the superiority of our proposed approach over other baseline methods, indicating the effectiveness of the typed iteration approach.