Research And Application Of Multi-dimensional Analysis On Hyperthyroidism Disease Data

With the rapid development of medical information, the traditional hospital digital information system and electronic health records system could not meet the requirements of mass medical data storage, analysis of medical data mining and diversified services. It is a subject that deserves research to help and improve the operation of the hospital. The big data analysis techniques used on patient disease data which could research the generation and development of hyperthyroidism disease is helpful to realize individual medical treatment of patients with hyperthyroidism.Hyperthyroidism is a kind of disease with long time treatment, many complications and serious harm to human health. In recent years, the incidence rate is increasing. Hyperthyroidism disease data including clinical examination data, medication data, diagnostic data, etc.. Behind these data, the changes of the disease and the knowledge of drug efficacy in the treatment of patients with hyperthyroidism could be discovered, which have important significance in the analysis of patients with hyperthyroidism.This paper mainly sketches the application of multidimensional data analysis technology in clinical data of hyperthyroidism disease, and constructs a multi-dimensional analysis system for clinical data of hyperthyroidism. The analysis of data mining, such as the test index and the use case of drugs are supported in this system. Specifically, the main work of this paper includes as following:First, this paper describes the design framework of the multi-dimensional analysis system of hyperthyroidism disease. According to the specific characteristics of hyperthyroidism disease data, this paper presents a systematic framework and system framework, and focuses on the three main hyperthyroidism doctor’s advice module and drug analysis module. The preprocessing module is designed to eliminate the noise data, and the data is extracted from the raw data and loaded into the designed system. The other two modules are responsible for the completion of the corresponding analysis.Then, this paper analyzes the changes of clinical indicators of hyperthyroidism. Firstly, the data of each index is formed in order to form multi-dimensional data stream, and the average degree of the index data flow is calculated by the similarity algorithm. After the regression analysis and correlation analysis, the correlation between the indicators were obtained. Analysis and data mining of drug use have been carried out in the treatment of hyperthyroidism. In order to study the effect of drug on the disease of patients with hyperthyroidism, this paper has divided the treatment of hyperthyroidism into several stages, then analyze the effects of three kinds of major drugs on the incidence of hyperthyroidism and the proportion of patients with hyperthyroidism.Finally, with the implementation of Apriori algorithm based on HANA, we have completed the index and disease, and the age of the patients were associated with the rules mining. And also some association rules of the drug and disease, medication and age are carried out at last.In this paper, beginning in the design and construction of the multi-dimensional analysis system of the hyperthyroidism disease, the data is loaded into the analysis system, then the system is deployed on the data analysis platform of HANA. The analysis and mining algorithm is also based on this platform, and displayed the results on WebI from SAP.