Viral hepatitis B has become a worldwide public problem since it was discovered in the sera of Australian aboriginals in the 1960s. In addition, viral hepatitis B has the characteristics of a long course, difficult to cure, and recurring attacks. At present, western medicine adopts symptomatic treatment strategies including antiviral, liver protection and enzyme reduction, yellowing and improvement of body immunity, etc. However, long-term use of antibiotics is prone to drug resistance and adverse reactions to the kidneys. Compared with Western medicine's symptomatic treatment strategies, Chinese medicine prescriptions, such as Yinchenhao Decoction, Sini San, Ganlu Xiaodu Dan, etc., have the characteristics of multi-flavored Chinese medicine and multi-component synergistic effect, simultaneously targeting multiple targets and multiple pathways. It is easier to get wide affirmation from clinicians and patients. As a manifestation of physicians' clinical experience, clinical electronic medical records include a large amount of effective diagnosis and treatment knowledge, which is a valuable source of knowledge for clinical decision-making assistance. The entity uniqueness and relationship display of the knowledge map are consistent with the characteristics of traditional Chinese medicine knowledge that need to be named uniformly and emphasize the relationship. Using the knowledge map as the organizational form of traditional Chinese medicine knowledge will be able to well present the overall network of knowledge, and in the knowledge The function of clinical decision-making assistance is realized under the support of reasoning. This article takes viral hepatitis B as an example, carries out effective knowledge extraction from hepatitis B electronic medical record data, merges to form structured knowledge, and constructs a knowledge map of traditional Chinese medicine diagnosis and treatment of hepatitis B. Driven by the rules of knowledge reasoning, it provides knowledge support for intelligent diagnosis and treatment, auxiliary analysis, and decision support; summarizes and displays the experience of Chinese medicine diagnosis and treatment, so that inexperienced doctors can better learn and inherit. © 2021 IEEE.

Objective: To summarize and analyze the addition and subtraction rules and applicable symptom clusters of Banxia xiexin decoction Decoction by Professor Lei Zhang, so as to provide references for doctors with insufficient clinical experience and to pass on the experience of famous doctors. Methods: from professor Zhang from 2006 to 2017, 11038 a basis to establish the database, according to the inclusion criteria extraction using Banxia xiexin Decoction, 268 cases of heart, based on the theory of formal concept analysis, using the attribute partial order principle to differentiate different types of prescription, it is concluded that Banxia xiexin Decoction add and subtract method, analysis of Banxia xiexin Decoction applicable symptom clusters. Results: In this paper, it was concluded that there were 7 kinds of symptom clusters and 6 kinds of prescription addition and subtraction. Conclusion: Professor Lei Zhang makes good use of Banxia xiexin Decoction to treat diseases such as distention and fullness, abdominal distension, stomachache, oral soreness and insomnia. © 2020 IEEE.

In this paper, the association relationship between data is becoming more and more complex. The Apriori algorithm, needs to scan the database several times to calculate the candidate set, which is inefficient. By designing the experiment, the use of professor Lei Zhang from 2006 to 2017, 11038 cases of the basis data set, to put forward four kinds of improved algorithm of association rules: FP-growth algorithm, Apriori_ParallelCount algorithm, GAN algorithm, Apriori_BOMS algorithm do the performance testing. Experimental results show that the Apriori_ParallelCount algorithm has the best comprehensive performance among the five association rule algorithms. Apriori_ParallelCount algorithm is uses the hash stored to nature, the data collection of transaction and the mapping of frequent item sets is stored in a hash structure, to avoid the algorithm when calculating the candidate set support multiple scan data set, proposed multi-threaded operation, improve the efficiency of the algorithm. Using the most efficient algorithm to contain 1625 cases of basis of Pinellia tuber and Golden thread data association rules analysis, it is concluded that professor Zhang Lei the synergy law of Pinellia tuber- Golden thread, using the Python language for visualization mapping of all analytical results. © 2020 IEEE.

Objective: To summarize Professor Lei Zhang's clinical experience in treating asthma. Technology or Method: The 11038 cases of Professor Zhang Lei from 2006 to 2017 were digitization, and the clinical case database of Professor Zhang was established. Three professional researchers extracted medical records of asthma syndrome from the database and established the prescription database of asthma syndrome. Finally, R Studio platform is used to apply association rules, hierarchical clustering and other machine learning methods to study prescription medical cases. Results: 260 medical cases, 238 related prescriptions and 323 kinds of Traditional Chinese medicine were excluded function. It is emphasized to grasp the physiological characteristics of the lung, pay attention to the characteristics of 'purge and purge', and the identification and treatment of asthma syndrome are complex, especially the dosage and compatibility of drugs should be paid attention to in clinical practice. and included Ephedra, Bitter apricot seed and Perilla fruit are the three drugs with the highest frequency of use. Among them, 40 traditional Chinese medicines have higher than average frequency of use. Cluster analysis of 9 practical combinations of symptoms and Traditional Chinese medicine. Conclusion: Professor Zhang believes that lung diseases, weak lung Qi, sputum is the root of asthma syndrome. Asthma, oppression in Chest, cough and other diseases are commonly seen in the treatment of asthma syndrome due to evil Qi blocking lung, obstruction of Qi and loss of lung. © 2020 IEEE.