## 3 Our Proposed MDA
### Medical Knowledge Graph Module  
The articles of the open-source medical websites consist of structured information and semi-structured information, such as haodaifu, 39 net, etc. Such information includes disease description, etiology, symptoms, complications, prevention, drugs, examination, and treatment methods. A medical extraction framework in the medical knowledge graph module is designed to extract the information from open-source medical websites and transform it into a medical knowledge graph composed of triplets. In this section, the detailed framework of information extraction in the medical field is shown in Figure 1, and it is structured into four parts: website source, web-crawl, extraction, and generation of triples. First, many open-source medical websites are analyzed for the structure of the pages [43]. In addition, we propose different extraction schemas that depend on the composition of the content on the website. Second, medical knowledge from the website is crawled and stored as text, which consists of structured information and semi-structured information. Then, in the extraction parts, the structured information can directly utilize the method of segmentation and extraction to obtain medical knowledge. For example, the structured text 'drugs for cerebral infarction: recombinant tissue plasminogen activator, urokinase' ( [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ &  
EDA [44] only focuses on the diversity of augmented data and ignores the relevance of words in sentences, and it includes synonym replacement (SR), random insertion (RI), random swap (RS), and random deletion (RD). Table 1 shows a few examples of EDA from the original data: RS randomly selects consecutive words in the sentence and replaces them with words of the same type, such as heart disease and pneumonia; RI inserts adverbs or adjectives into sentences at random, such as words'seriously'; RS randomly chooses two different words from the sentence and swaps their positions to generate augmented data; RD randomly removes any number of words from the sentence. Obviously, EDA does not require complex pre-training language models, and it is simpler than other data augmentation methods. However, the shortcomings of EDA are also obvious. First, because the original dataset **S** contains text data **x** and label data **y**, EDA only augments text data by keeping label data **y** consistency, but occasionally leads to text data to be incompatible with label data. Second, EDA is designed to generate large amounts of text data, which causes a sentence to abandon part of its semantics. In addition, the performance of the EDA is not effective for the text data of a specific domain. To alleviate these problems, the medical text data augmentation module is designed with four methods that include medical knowledge replacement (MKR), words insertion (WI), words swap (WS), and words deletion (WD). To be specific, the medical text data augmentation module exploits the medical knowledge graph to identify medical terms and alleviate compatibility between texts and labels. Moreover, examples of augmented sentences are shown in Table 2.  
In the MKR method, keywords are defined as words that are related to label data in text data. Each keyword in each label \(y_{i}\) is fed into the medical knowledge graph to obtain the medical knowledge that is associated with the keyword. Then, word that is related to the keyword is replaced with word from the acquired medical knowledge. It can be seen from Table 2 that an original data \(\textbf{S}_{ori}=\{\textbf{x}_{ori},\textbf{y}_{ori}\}\) is given, where text data \(\textbf{x}_{ori}\) represents the text 'the patient with the cerebral hemorrhage had a headache' (  
In the WI method, adverbs and adjectives of different diseases and symptoms are selected from the medical knowledge graph. In addition, different types of keywords such as disease and symptoms are obtained from the label data. After that, those selected words can be randomly inserted near the position of their associated keywords.