Application of mathematical modeling for prediction of complications of hypertension
https://doi.org/10.25881/18110193_2022_1_4
Abstract
Hypertension is a complex cardiovascular condition, defined as an abnormally high blood pressure. Such long-term and consistent increase in blood pressure could result in coronary heart disease, stroke, kidney damage and other serious debilitating conditions. Complication rate from hypertension depends on how well you can predict and prevent those complications, considering individual patient’s risks. Several mathematical models and computer algorithms that are currently used for these purposes have relatively low accuracy and prognostic value. Machine learning methods could be a next step in improving outcomes of patients with hypertension in terms of calculating their individual risk of complications and choosing rational therapeutic strategy based on that data. We performed a literature review to cover the topic of machine learning methods in the management of patients with hypertension.
About the Authors
K. O. Tutsenko
Krasnoyarsk State Medical University named after prof. V. F. Voino-Yasenetsky»
Russian Federation
Russian Federation
Tutsenko K.O.
Krasnoyarsk
A. N. Narkevich
Krasnoyarsk State Medical University named after prof. V. F. Voino-Yasenetsky»
Russian Federation
Russian Federation
Narkevich A.N., Dr. Sci. (Medicine)
Krasnoyarsk
D. A. Rossiev
Krasnoyarsk State Medical University named after prof. V. F. Voino-Yasenetsky»
Russian Federation
Russian Federation
Rossiev D.A., Dr. Sci. (Medicine), Professor
Krasnoyarsk
O. V. Ipatyuk
UN «Palmira»
Russian Federation
Russian Federation
Ipatyuk O.V.
Krasnoyarsk
S. M. Avdeev
Individual entrepreneur Avdeev Sergey Maksimovich
Russian Federation
Russian Federation
Avdeev S.M.
Krasnoyarsk
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Review
For citations:
Tutsenko K.O., Narkevich A.N., Rossiev D.A., Ipatyuk O.V., Avdeev S.M. Application of mathematical modeling for prediction of complications of hypertension. Medical Doctor and Information Technologies. 2022;(1):4-11. (In Russ.) https://doi.org/10.25881/18110193_2022_1_4
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