## 6 Conclusions and Prospects  
To address the difficulty of obtaining misdiagnosed medical records, a data augmentation approach based on hard-negative samples consisting of fusing SimBERT and a knowledge graph is proposed in this paper. Firstly, we selected 40 misdiagnosed cases of diabetic complications as the original data before data augmentation. Secondly, we divided the contents of electronic medical records into two parts. The key symptoms identified by the model as negative samples formed one part of the core symptom phrases of incorrectly diagnosed cases chosen by specialists. The SimBERT model directly transformed these core symptom phrases into new symptom phrases. Other noncore phrases were very similar to the positive samples. To find texts, we used Top-K sampling and compared their cosine similarities with the embedding vector of the knowledge graph entities. Finally, 160 enhancement samples were obtained by combining the generated texts of the two parts and the disturbed numerical indexes.  
Figure 6: Decision curve analysis for each dataset.  
However, this paper still suffers from some shortcomings: First, the model's enhancement effect on the diabetic peripheral neuropathy dataset was not noticeable, indicating that the data augmentation for misdiagnosed medical records for this dataset did not obtain enough training features. Second, data augmentation could not wholly replace the richness of the original misdiagnosed medical records, and the training information provided to the model still needed to be improved. Third, some complications of diabetes require image features for diagnosis. At the same time, this work does not involve multimodal features, so determining how to combine images for data augmentation is a worthwhile research direction. In the future direction of our work, firstly, we should increase the richness of the original data as much as possible so that the augmented data has more training information. Secondly, we can utilize methods such as adversarial training to perturb the positive samples to increase the feature information of the negative samples; thirdly, we can give the features of the hard negatives more weight through the innovation of the loss function, and lastly, we can construct the training features for the multimodal joint embedding a neural network model. These are the focuses of our future work's direction.  
Conceptualization, S.L. and X.Y.; methodology, S.L.; software, S.L.; validation, S.L., X.Y. and P.C.; formal analysis, X.Y.; investigation, X.Y.; resources, X.Y.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, X.Y.; visualization, X.Y.; supervision, P.C.; project administration, P.C.; funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Key Research and Development Program of China (Grant No. 2016 YFC0802500).  
Not applicable.  
The data are openly available at [[https://tianchi.aliyun.com/dataset/datadetail?dataId=88836](https://tianchi.aliyun.com/dataset/datadetail?dataId=88836)]([https://tianchi.aliyun.com/dataset/datadetail?dataId=88836](https://tianchi.aliyun.com/dataset/datadetail?dataId=88836)), accessed on 17 February 2024.  
The authors declare no conflicts of interest.