## 3 Experiments
### Ablation Study  
In our model, in view of the particularity of Chinese medical data, we used pre-trained medical character embedding combined with pre-trained medical word embedding as the input of the SRAGNNs model, which improves the performance of the entire model. In order to better capture the correlation between relations, we used the attention mechanism when using GNNs. We conducted ablation experiments to prove the effectiveness of word embedding in the model and the effectiveness of the attention mechanism in relation classification. We checked the influence of the number of layers of the GNNs on the performance of the model. At the same time, the effectiveness of the relation-based attention-guided graph neural network layer in the model is verified. The experimental results are shown in Table 7.  
In order to verify the impact of word embedding on sentence context features, we deleted the word embedding in the input layer in the SRAGNNs model with two layers of GNNs. In order to verify the influence of the attention mechanism in Relation-Based Attention-guided Graph Neural Networks on the model, we deleted the attention mechanism in the GNNs. To verify the effectiveness of the relation-based attention-guided graph neural networks layer in the model, we removed this module from the model. When the latent relation prediction layer is removed, each relation in the predefined relation set is used to construct the specific relation feature vector. In order to see the influence of the GNNs layer number on the model performance, we set the GNNs layer number to 1, 2 and 3, respectively. Through the experimental results, we found that when the GNNs has two layers, the model performance is better. The attention mechanism in word embedding and relation classification also has a great influence on the performance of the model. As shown in the experimental results in Table 7, without the relation-based attention-guided graph neural \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **N = 1(\%)** & **N = 2(\%)** & **N = 3(\%)** & **N = 4(\%)** & **N \(\geq\) 5 (\%)** \\ \hline CopyRe & 59.37 & 49.33 & 43.62 & 46.19 & 25.49 \\ GraphRel & 64.61 & 54.83 & 49.37 & 48.31 & 32.86 \\ HRL & 70.87 & 62.21 & 56.58 & 58.93 & 39.87 \\ ETL-Span & 71.83 & 68.12 & 57.64 & 69.44 & 57.73 \\ WDec & 67.66 & 70.20 & 68.35 & 75.67 & 43.50 \\ RSAN & 67.83 & 71.54 & 74.22 & 80.92 & 62.41 \\ BERT-JEORE & 74.21 & 72.36 & 75.47 & 80.34 & 64.72 \\ SRAGNNs(\#layers = 2) & **76.18** & **74.53** & **77.46** & **81.40** & **66.93** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Relation extraction on sentences with different numbers of triplets. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Datasets** & **Train Sentences** & **Dev Sentences** & **Test Sentences** \\ \hline
**SRAGNNs(\#layers = 2)** & **76.44** & **74.35** & **75.30** \\ - Word Embedding & 74.59 & 72.99 & 73.78 \\ - Attention & 69.42 & 73.20 & 71.26 \\ - RAGNN Layers 1 & 63.30 & 67.66 & 65.41 \\ SRAGNNs (\#layers = 1) & 75.81 & 73.82 & 74.80 \\ SRAGNNs (\#layers = 3) & 75.70 & 74.15 & 74.92 \\ \hline \hline \end{tabular}  
* Relation-Based Attention-Guided Graph Neural Networks Layers.  
\end{table} 
Table 7: Ablation study of SAAGNNs on CEMR dataset. networks layer, the \(F1\) score of the SRAGNNs model is significantly reduced. This is due to the fact that there are no potential relations in the sentence, resulting in an increase in the number of predicted entity pairs, which will greatly increase relation redundancy. With the increase in the number of medical relations in Chinese EMR, the training and inference time also increases substantially. Through this experiment, the effectiveness of several modules in the SRAGNNs model is demonstrated, which is beneficial to the improvement of the overall experimental performance. From Table 7, we can see that word embedding also has a certain impact on the performance of the model.