DOI:https://doi.org/10.15407/kvt201.03.065
Cybernetics and Computer Engineering, 2020, 3(201)
KIFORENKO S.I.1, DSc (Biology),
Leading Researcher of the Application Mathematical and Technical
Methods in Biology and Medicine Department
e-mail: skifor@ukr.net
VASYLIEV I.Yu.2, PhD (Mathematics),
Senior Researcher of the Mechanics and Mathematics Department
e-mail: igor_v@univ.kiev.ua
ORLENKO V.L.3, PhD (Medicine), Senior Researcher,
Head of Scientific-advisory Department of Ambulatory
and Preventive Care for Patients with Endocrine Pathology Department
e-mail: orleva@ukr.net
IVASKIVA K.Yu.3, PhD (Medicine),
Senior Researcher of the Scientific-advisory Department of Ambulatory
and Preventive Care for Patients with Endocrine Pathology
e-mail: k_iva@ukr.net
OBELETS T.A.1, PhD student,
Junior Researcher of the Application Mathematical and Technical
Methods in Biology and Medicine Department
e-mail: obel.tet@gmail.com
1 International Research and Training Centre for Information Technologies
and Systems of the NAS and MES of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine
2 Taras Shevchenko National University of Kyiv,
4e, Acad. Glushkov av., Kyiv, 03127, Ukraine
3 State Institution “V.P. Komisarenko Institute of Endocrinology
and Metabolism of NAMS of Ukraine”,
69, Vyshgorodska str. Kyiv, 04114, Ukraine
HIERARCHICAL SIMULATION. ALGORITHM FOR PREDICTION OF GLYCEMIC PROFILE FOR DIABETES
Introduction. Diabetes mellitus, a common chronic disease, requires lifelong treatment and, like any chronic disease, requires regular monitoring and self-control at home. Revolutionary changes in glycemic control in diabetic therapy have occurred thanks to the development of sensors for continuous glucose monitoring (CGM), which can, almost continuously, measure the concentration of glucose in the subcutaneous tissue. The most common barriers to CGM use are related to high device costs and lack of insurance coverage for their purchase, alleged sensor inaccuracy, anxiety, which is associated with dislike of wearing the device. Thus, sensors are good but expensive, not affordable for everybody and could be uncomfortable. Therefore, the constant search for alternative solutions remains an important challenge.
The purpose of the article is to show the possibility of using hierarchical modeling technology to develop and study glycemic profile prediction algorithm as, to some extent, alternative to continuous monitoring sensors in a context of limited irregular measurements.
Results. The program-algorithmic structure for realization of the concept of hierarchical simulation is developed. The possibility of conducting research on models of varying complexity is shown. An algorithm for insulin-glucose tolerance test was synthesized. A procedure for predicting the daily glycemic profile by analytical formulas has been developed, which provides an opportunity to assess the trend of glycemic dynamics as an addition to the irregular glucose measurements with a glucometer. A simulation study, the result of which is the visualization of glycemic profile in a context of expected food intake schedule and compensating insulin doses obtained by the analytical algorithm, was conducted.
Conclusions. The proposed hierarchical modeling technology, based on the use of mathematical models of varying complexity, allows to conduct a complex of simulation studies to correct glycemia in diabetes at the preclinical and pre-ambulatory stages. During the simulation of forecasting procedure, configuration discrepancies of the glycemic profile obtained from different models were detected, but they are within the margin of error and reproduce the main trend in the dynamics of glycemia during meals and insulin injections. The calculated bolus doses of insulin are almost identical to those used by insulin-dependent patients. The simplicity of calculations using analytical formulas can be a prerequisite for the implementation of the algorithm in a special-purpose portable autonomous devices or in applications for Android OS.
Keywords: hierarchical simulation, glycemic control system, identification algorithms control forecasting, simulation preclinical trials.
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Received 03.03.2020