Big Data Analytics with Advanced Data Visualization of Medical information system qMS records is presented. The inpatients with Diabetes Mellitus type 1 were chosen for analysis. The various methods of analysis and visualization were implemented: Gray reflected binary code (Java), Cluster analysis (iPython), Graph analysis (iPython, Gephi), 3D Visualization (Java), as well as supercomputer “Uran” was used. The connected pathogenetic Continuum of Diabetes Mellitus type 1 was built. The Continuum of Diabetes Mellitus type 1 progression allows assume that Parathyroid hormone-related protein (PTHrP) plays the critical role in multiorgan pathogenetic cascade in this disease, including the development of Lung cancer. Based on our study we suggest considering PTHrP in terms of pharmacological treatment. Big Data Analytics including Cluster and Graph Analysis of Medical information system's data flow can be used to study pathogenesis of the disease and for new drugs creation proposal.
Olga Kolesnichenko, Mikhail Chernoskutov, Vladimir Averbukh, Igor Mikhaylov, [et al.], Big data analytics of inpatients flow with diabetes mellitus type 1: revealing new awareness with advanced visualization of medical information system data // IEEE Xplore Digital Library. 29 July 2019. Proc. 9th Intern. Conf. on Cloud Computing, Data Science and Engineering, Confluence, 10-11 Jan. 2019, Noida, India, Inst. Electrical and Electronics Engs Inc. P. 191-196.
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