Cloud Computing Support for Diagnosing Researches

Authors: A. Amirov, O. Gerget, V. Kochegurov

Abstract:

One of the main biomedical problem lies in detecting dependencies in semi structured data. Solution includes biomedical portal and algorithms (integral rating health criteria, multidimensional data visualization methods). Biomedical portal allows to process diagnostic and research data in parallel mode using Microsoft System Center 2012, Windows HPC Server cloud technologies. Service does not allow user to see internal calculations instead it provides practical interface. When data is sent for processing user may track status of task and will achieve results as soon as computation is completed. Service includes own algorithms and allows diagnosing and predicating medical cases. Approved methods are based on complex system entropy methods, algorithms for determining the energy patterns of development and trajectory models of biological systems and logical–probabilistic approach with the blurring of images.

Keywords: Biomedical portal, cloud computing, diagnostic and prognostic research, mathematical data analysis.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1093874

References:

[1] Konstantinova L.I. Kochegurov V.A.,Shumilov B.M. Parametric identifying non–linear differential equations based on spline schemeand polynomial// 1997.– №5.– P. 15–20.
[2] Rotov A.V., Pekker, I.S., Medvedev M.A., Berestneva O.G. Adaptive characteristics of a human (assess and prognose). –Tomsk:Pub. TPU, 1997.
[3] Gerget O.M., Kochegurov V.A. Actual medical provlems solving using mathmatical methods/ LAP LAMBERT Academic Publishing GmbH & Co. KG, LAP, 2012, 1 – 145.
[4] Neural networks advantages // Artificial intelligence portal. URL: http://www.aiportal.ru/articles/neural–networks/advantages.html.
[5] Zagoruiko N.G., Samokhvalov K.F., Sviridenko D.I. Empiric research logic. Novosibirk: Nayka, 1985.
[6] Gelfand I.M., Rosenfeld B.I., Shifrin M.A. Essays about working together mathematicians and physicians.–N.:Nayka,1989.
[7] Yankovskaya A.E., Matrosova A. Yu., Strizhov M.A. The Logical Probabilistic System of Pettern Recognition// Proceedings of the Pettern Recognition and Image Understanding . 5th Open German- Russian Workshop.– Germany. Herrshing.–1999.– pp. 298-305.
[8] Yankovskaya A.E., Gerget O.M. Intelligent subsystem logic and probabilistic recognition of blurring of images. Informational systems and technologies.–Novosibirsk,2000.–P. 548–551.
[9] Yankovskaya A.E. The degree of implication and partial orthogonalization disjunctive normal Boolean functions in connection with the problem of decision–making // All–Siberian Readings on Mathematics and Mechanics, 1997.– Т.1.– Tomsk: Pub. TGU, 1997.– P.225–231.
[10] Genkin A.V., Dubner P.N., Petergao E.V. Subsystem forecasting indicators of child health in medical information systems / ACS designing problems.– Minsk, 1979.– № 37/3.– P.123–125.