## 4 Performance Analysis
### Datasets  
The experiments are carried out on CCGS2019 dataset, CHIP2020 dataset, BITEmrNER dataset, and BITEmrRC dataset. In addition, we evaluate the MDA on the NER task and the RC task. Moreover, the structures of these datasets are described in detail as follows:  
Figure 2: The prototype of the data augmentation process.  
* CKKS2019 is a dataset for the Chinese electronic medical record NER task of the 13 th China Conference on Knowledge Graph and Semantic Computing which aims to provide a platform for researchers and application developers to test technologies, algorithms, and systems, and it consists of 1379 medical records that include six categories of entities, namely anatomy, disease, imaging examination, laboratory examination, drug, and operation. During the fine-tuning process, CKKS2019 is divided into a training dataset and a testing dataset in a certain proportion;
* CHIP2020 is a Chinese medical dataset for the RC task of the 6 th China Health Information Processing Conference which is an annual conference on biological information processing and data mining, which contains 43 categories of pre-specified relations, 17,000 Chinese medical sentences, and 50,000 triplets. Moreover, the dataset consists of 518 pediatric diseases and 109 common diseases. In addition, the CHIP2020 includes more than ten categories of entity, such as symptoms, imaging examination, etc. Moreover, to enhance the normalization of the dataset, the CHIP2020 is divided into a training set and a testing set in the official method;
* BITEmrNER is a Chinese medical dataset for the NER task collected by the BIT laboratory, and it consists of 1200 electronic medical records with cerebrovascular disease from the First Hospital of Zhejiang Province and the Fourth Affiliated Hospital Zhejiang University of Medicine. Furthermore, the BitEmrNER consists of the medical description text data and label data, where the text data include the history of present illness and past medical history, and the label data include different categories of entities. In this study, we randomly select 900 samples from the BITEmrNER to augment the training set;
* BITEmrRC is a Chinese medical dataset for the RC task collected by BIT laboratory, and it consists of electronic medical records with cerebrovascular disease from the First Hospital of Zhejiang Province and the Fourth Affiliated Hospital Zhejiang University of Medicine. In addition, the BITEmrRC contains more than 40 categories of pre-specified relations, 2400 Chinese medical sentences, and more than 8000 triplets. In this study, the BITEmrNER randomly selected 1600 samples from the BITEmrNER to augment the training set.  
Therefore, it can be seen from Table 3 that the statistics of these datasets are listed to introduce the characteristics of CKKS2019 dataset, CHIP2020 dataset, BITEmrNER dataset, and BITEmrRC dataset. In fact, BITEmrNER and BITEmrRC datasets that are from the same set of electronic medical records and annotated for different tasks are electronic medical record datasets constructed by the BIT laboratory. However, the public datasets CCKS2019 and CHIP2020 pay attention to different diseases, and researchers can compare the performance of different models on the public dataset. In addition, the samples of these datasets are provided in Table 4.  
After that, these datasets are employed in the different NLP tasks to verify the performance of MDA.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Statistics**} & \multicolumn{2}{c}{**CCKS2019**} & \multicolumn{2}{c}{**CHIP2020**} & \multicolumn{2}{c}{**BITEmrNER**} & \multicolumn{2}{c}{**BITEmrRC**} \\ \cline{2-7}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline Sentence & 1000 & 379 & & 3585 & 900 & 300 & 1600 & 800 \\ Task & NER & RC & & NER & & RC \\ Categories & 6 & & 43 & & 6 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of datasets.