## 2 Related Research
### Deep Learning Approach  
With the application of deep learning methods in various fields, researchers have started to apply them to biomedical relation extraction work. Currently, CNN, LSTM, and Transformer are the mainstream methods in the field of medical entity relation extraction. [PERSON] et al. [20] proposed a convolutional neural network architecture with multi-pooling operations for medical relation classification of clinical records and explore a loss function with a category-level constraint matrix. [PERSON] et al. [21] designed a new paragraph attention mechanism based on a convolutional neural network, extracted semantic local features through word embedding, and then connected different embedding features to classify relations. This method achieved good results in the Chinese Herbal Disease and Herbal Chemistry (HD-HC) dataset. [PERSON] et al. [22] proposed a hybrid model including a converter-based encoding layer, an LSTM entity detection module, and a reinforcement learning-based relation classification module, which greatly reduces the transmission of errors in relation extraction by deep learning methods. [PERSON] et al. [23] proposed the first model based on a recurrent neural network to classify relations in clinical records. This method also explored the differences between different contexts in sentences and evaluated the influence of word embedding on the performance of the LSTM model. [PERSON] et al. [24] proposed an improved binomial tree LSTM model that combines word vectors with features such as location and syntactic information. The model provides multiple patterns for the detection and classification of drug interaction relations. The F1 value of relation detection reached 83.8%, and the F1 value of relation classification reached 73.5% in the DDI2013 evaluation data. [PERSON] et al. [25] proposed a remotely supervised sentence-level attention-based convolutional neural network relation extraction model. The model uses CNN to convert each sentence of the input into a sentence vector, then adds an attention mechanism between sentences, and, finally, adds sentence information based on the removal of noise, significantly improving the performance of the model. [PERSON] et al. [26] used BERT (pre-trained language model) as a bi-directional encoder pair to extract entities and relations from biomedical and clinical records, and their proposed approach achieved advanced performance on many biomedical and clinical datasets compared to other model systems. [PERSON] et al. [27] introduced biomedical domain knowledge containing conceptual information such as proteins and compounds based on BERT, and assigned weights to feature representations through Gaussian probability distributions; the model achieved an accuracy of 76.56% on the CPI dataset. [PERSON] et al. [28] used BERT for the relation extraction task. The method explored the way entities and entity locations are combined in the pre-trained model. Graph neural network-related techniques can be combined with deep learning to efficiently process graph-type input data. In the biomedical field, research on relation extraction using graph neural networks for graph structure representation learning has emerged. The graph neural network can learn the dependency relations between words in a sentence to better explore the whole information of the sentence [29]. [PERSON] et al. [30] combined a graph recurrent neural network (GRN) based on BiLSTM, and the graph-based neural network architecture can better model biomedical sentences with complex hierarchical structures and effectively improve the feature extraction ability of the model. [PERSON] et al. [31] proposed an attention-guided graph convolutional network (AGGCN) model with a full dependency tree as input, which makes full use of the information in the dependency tree in order to better extract the correlations. [PERSON] et al. [32] proposed an end-to-end approach based on a bidirectional tree structure for long short-term memory.