## 1 Introduction
Therefore, data augmentation for misdiagnosed medical records should first solve the problem of defining the hard negatives. The training data obtained from data augmentation should be able to meet the definition of hard negatives because 95% of simple negative samples are unnecessary and insufficient, and only 5% of hard negatives are necessary and sufficient for improving accuracy in a given task ([PERSON] et al., 2022) [9]. Currently, tasks such as text recommendation, information retrieval, word embedding, and knowledge graphs in natural language processing use hard-negative sampling to improve the safety performance of models. [PERSON] et al. (2022) employed multiple hard negatives in text comparison learning to improve model performance. They differentiated between positive and hard-negative examples by comparing the similarities in different sentences [10]. [PERSON] et al. (2021) proposed the ANCE training approach, which involves continuously recording an entire document library according to the current updated model during the training process and then selecting texts with high similarity levels to query, while avoiding the selection of positive samples as negative examples [11]. [PERSON] et al. (2022) designed an improved graph convolutional network to acquire the feature vectors for circRNA and diseases and reduce the influence of noisy samples. The cited authors selected negative samples based on the similarity of the graph representations of circRNA and achieved better prediction performance [12]. It can be seen that a hard negative is the closest negative sample to the anchor or positive sample, i.e., the most similar sample in the embedding space. When using data augmentation to improve model robustness based on misdiagnosed medical records, the spatial similarity distance of augmented data from anchor points and positive samples must be considered to provide more gradient information to the model. However, there are three approaches for data augmentation: interpretation-based, noise-based, and sampling-based approaches. The last two methods cannot guarantee that the results are similar to the original samples or that the generated results differ from the original sample labels. Applying data augmentation to negative samples of medical electronic records ensures that the results are similar to the actual samples and belong to the same label. The interpretation-based approach generates additional training samples while maintaining the original label, but direct generation will affect the similarity between the misdiagnosis and positive samples. Therefore, we need a new data augmentation approach for misdiagnosing medical records that can retain the negative sample labels and correspond to the definition of hard negatives. Specifically, our research objectives are as follows:  
To determine how to perform data augmentation without a sufficient number of misdiagnosed medical records;  
To determine how enhanced data can meet the requirements of a hard-negative sample and improve a model's security performance. A data augmentation approach for application to hard-negative samples consisting of fusing SimBERT, and a knowledge graph is proposed in this paper, and the contributions are as follows:  
We performed SimBERT generation using the core conditions in the text of the electronic medical records and generated non-core conditions through Top-K sampling of the similarity with the knowledge graph entities, thereby not only preserving the negative sample labels of the key variables of the misdiagnosed cases identified by the generative model but also the high similarity level of the overall samples about the positive samples, in addition to incorporating diabetic complications into the generative task using knowledge-graph-related domain knowledge.