## 1 Introduction
### Graph Neural Networks Based extraction Model  
In recent years, graph neural networks (GNNs) [19] have been widely used in the research of entity and entity relationships of news text. Later [PERSON] et al. [20] replaced the Almeida-Pineda algorithm with a more general backpropagation and proved its effectiveness. Subsequent efforts have improved computational efficiency through local spectral convolution techniques [21; 22]. [PERSON] et al. [23] proposed a graph neural network with generation parameters (GP-GNNs) used in relation extraction. [PERSON] et al. [24] presented the Graph Attention Networks (GATs), which sums neighborhood states using self-attention  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sentence Type** & **Sentence** & **Entity Pairs and Relation Triplets** \\ \hline EPO &
\begin{tabular}{c} **(**\#H**\#  
layers. [PERSON] et al. [25] apply GATs to recognize relations between entities and directly uses the complete dependency tree as the model's input, which improves the performance of relation extraction. GraphRel [26] is proposed as a two-stage joint model based on a graph convolutional network (GCN).  
In this work, we propose a Specific Relation Attention-guided Graph Neural Networks (SRAGNNs) model to jointly extract medical entities and relations in Chinese EMRs. Concerning the inner connection between different relations, we use the attention-guided GNNs to learn the correlation, and utilize multi-head attention to capture interactions between different relation types. For relation extraction, we regard it as a multi-label classification task. We believe that the relation extraction in a sentence should consider the influence between each other. Thus, the GNNs in our model are guided by attention in order to learn the correlations and dependencies between relations. By using the same multi-head attention to capture the interaction between different relation types, the model can understand the relevant semantics of each relation. Moreover, combined with the sentence context features captured by the transformer from word embedding and character embedding, the correlation strengths under different relation types are calculated respectively, and the relation classifiers with interdependent sentence meanings are learned. We use feature vectors based on specific relations to construct a particular sentence representation under each relation, and then perform sequence labeling to extract entities corresponding to specific relations.  
To output the labels of medical entities and the relations between them in Chinese EMRs, we adopt the entity labeling scheme proposed by RSAN to simplify the entity labeling process. We merge the head and tail entities [H, T] in the triplet into a typical BIES sign (_begin, inside, end_, or _single_) as our entity label. We will generate separate tag sequences for sentences with multiple triplets based on different relations. In the tag sequence of a specific relation, only the corresponding head and tail entities will be labeled, and the remaining characters will be assigned the tag \"O\".  
Our contributions to this paper can be concluded as follows:  
* To propose a Specific Relation Attention-guided Graph Neural Networks (SRAGNNs) model for the joint extraction of Chinese medical entities and relations. This model captures the fine-grain semantic features between relation and Chinese word characters and can extract overlapping triplets. The model uses relations to guide entity recognition.
* The model learns the correlation between various relations through attention-guided GNNs. At the same time, by combining the sentence context features encoded by the transformer, it fully captures the relevant semantics and transforms the relation extraction into a multi-label classification task.
* For the Chinese medical datasets, we use the encoding strategy of Chinese character embedding combined with word embedding to obtain the latest results on a medical evaluation dataset and a manually labeled Chinese EMR dataset, which proved to be effective.