The methods of machine learning for real-time detection of abnormal values of the patient's vital signs are considered. The aim is to assess the risk of the disease with worsening of the patient's condition. The system is designed to monitor patients using expert assessments that are included in fuzzy logic rules to compare patient vitals signs with disease risk assessment. Deviation of values from the norm is identified as an "abnormal" class in order to determine the reasons for the worsening of the patient's condition. The integrated platform "m-Health" system for decision making with feedback control allows the patient to be mobile and their vital signs are mapping in the current mode.
This study aims at mathematical modeling of systemic factors threatening the sanitary and hygienic state of sources of water supply. It is well-known, that this state affects health of population consuming water from different water sources (lakes, reservoirs, rivers). In particular, water quality problem may cause allergic reactions that are the important problem of health care. In the paper, the authors present the mathematical model, that enables on the basis of observations of a natural system to predict the system's behavior and determine the risks related to deterioration of drinking water resources. As a case study, we uses supply of drinking water from Lake Sevan, but the approach developed in the study can be applied to wide area of adjacent problems.
This paper proposes a simple mathematical model of heating process of the human skin and adjacent inner layers with the LED radiation used in the prevention and treatment devices for various diseases. The problem takes into account the heat removal by blood flow to the vessels. It is shown that abnormal blood flow due to the compression of tissue can lead to severe heating of the body and its burn. This may result even from using small LEDs of 2,5-30 mW.
The mathematical model of metabolism process in human organism based on Lotka-Volterra model has beeng proposed, considering healing regime, nutrition system, features of insulin and sugar fragmentation process in the organism. The numerical algorithm of the model using IV-order Runge-Kutta method has been realized. After the result of calculations the conclusions have been made, recommendations about using the modeling results have been showed, the vectors of the following researches are defined.
This article offers a risk assessment of bronchial asthma development in children with atopic dermatitis by applying
fuzzy-set theory to accumulated statistical data. It is shown that with a view to executing the said task one should
exercise a complex approach involving factors such as “IgE level”, “existence of obstructions” and “burdened bronchial
asthma heredity of immediate relatives”. The obtained results will assist in making adequate and well-informed medical
decisions as well as facilitate the decrease of the risk of developing bronchial asthma in children with atopic dermatitis.