## 4 Results
### Comparative Experiment of Decision Curve Analysis  
At present, decision curve analysis (DCA) is widely used to measure clinical practicability [26]. In this approach, the theoretical relationship between the threshold probability of a disease and a false positive and a false negative is used to determine the value of a prediction model. Then, this theoretical relationship is used to derive the net income of the model under different threshold probabilities.  
We compared Simkg-BERT data augmentation and non-data augmentation models and constructed clinical decision curves for four diabetes complication data sets, as shown in Figure 6. Assuming that we choose to diagnose diabetes complications with a prediction probability of 80% and treat them, we can obtain higher net benefits from the SimKG-BERT data augmentation model than the non-data augmentation model for the four data sets. Taking diabetic ketoacidosis as an example, if the data augmentation model is used in the auxiliary diagnosis, 35 out of every 100 patients can benefit from it without harming the interests of any other person; only 26 out of every 100 patients can benefit from the model without data augmentation. It can be seen that the model gained additional medical security properties by augmenting the data with misdiagnosed medical records.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Disease Anchor** & **Positive Sample** & \begin{tabular}{c} **Misdiagnosis Medical** \\ **Record Sample** \\ \end{tabular} & \
\begin{tabular}{c} **Sample Generated by** \\ **SimKG-BERT** \\ \end{tabular} &
\begin{tabular}{c} **Sample Generated by** \\ **SimBERT** \\ \end{tabular} \\ \hline Diabetic foot & 0.794337306 & 0.743392729 & 1.233987545 & 2.587869883 \\ Diabetic nephropathy & 0.459021014 & 0.903346463 & 0.997960887 & 2.153633064 \\ Diabetic peripheral & 0.768186109 & 1.349266796 & 0.892801615 & 1.761943813 \\ Diabetic ketoacidosis & 0.657259209 & 0.757763853 & 0.818068272 & 2.175057 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average Euclidean distance between samples and anchor points.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Diabetic Peripheral**} & \multicolumn{3}{c}{**Diabetic Nephropathy**} & \multicolumn{3}{c}{**Diabetic Foot**} & \multicolumn{3}{c}{**Diabetic Ketoacidosis**} \\ \cline{2-11}  & \begin{tabular}{c} **Precision** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ \end{tabular} & \begin{tabular}{c} **F1** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ \end{tabular} & \begin{tabular}{c} **F1** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ \end{tabular} & \begin{tabular}{c} **F1** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ \end{tabular} & \
\begin{tabular}{c} **Recall** \\ \end{tabular} &
\begin{tabular}{c} **F1** \\ \end{tabular} \\ \hline No data augmentation & 0.6406 & 0.9919 & 0.7785 & 0.5500 & 0.9909 & 0.7097 & 0.7722 & 0.9918 & 0.8714 & 0.5817 & 0.9839 & 0.7355 \\ SimBERT & 0.6512 & 0.9816 & 0.7728 & 0.5836 & 0.9725 & 0.7225 & 0.7856 & 0.9916 & 0.8834 & 0.6239 & 0.9723 & 0.7639 \\ Simkg-BERT & 0.6524 & 0.9839 & 0.7846 & 0.6463 & 0.9636 & 0.7737 & 0.8079 & 0.9918 & 0.8938 & 0.6641 & 0.9775 & 0.7909 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative experiment regarding the data augmentation model.