Kosmacheva Elena Dmitrievna,Космачева Елена Дмитриевна,Slavinskij Aleksandr Aleksandrovich,Славинский Александр Александрович,Stavenchuk Tatyana Vladimirovna,Ставенчук Татьяна Владимировна
申请号：
RU2017105544
公开号：
RU0002661559C1
申请日:
2017.02.20
申请国别(地区):
RU
年份:
2018
代理人:
摘要：
FIELD: medicine.SUBSTANCE: invention relates to medicine, namely, in transplantology and cardiology, and can be used to determine the degree of risk of transplant rejection. Method involves identifying predictors. Using an ultrasound method of investigation, speckle-tracking echocardiography, reveal a complex of 13 predictors of cardiac transplant rejection: global peak systolic strain of left ventricular (GLSLV, -%), longitudinal left ventricular strain in 4 chamber position (A4C, -%), longitudinal left ventricular strain in a two-chamber position (A2C, -%), longitudinal left ventricular strain in three chamber position (A3C, -%), global peak systolic strain rate of left ventricle), (GLSTRLV, -c-), radial strain of left ventricular (RadSLV, %), radial strain rate of left ventricular, (RadSTRLV, c-), circular strain of left ventricular (CirSLV, -%), circular strain rate of left ventricular (Cir STR LV, -c-), twisting (twist, ), rotation of the apical segments of the left ventricle), (ROT APEX, °), rotation of basal segments of the left ventricle (ROT BASE, °), rotation of the middle segments of the left ventricle (rotation of the middle segments of the left ventricle ROT MID, °). Then, using the formula, using computer analysis, we calculate the risk of rejection of the cardiac transplant: where Z k – “output” data of the third layer for 4 groups, e is an exponent, i 1…8 – index of the location of the “output” data of the second layer, the neural network, j 1…13 – index of the location of the “output” data of the first layer of the neural network, X – “output” data of the first layer of the neural network, w j – weight 13 of the normalized values, w I – weight 8 of normalized values, e – thresholds. Formula combines three layers of a neural network: transform 13 predictors into “normalized” values of the first layer of the neural network and get 13 “normalized” values (j). Transform the values of the first layer of the neural network into the second layer of the ne
