Pushkin Aleksandr Sergeevich (RU),Пушкин Александр Сергеевич (RU),Shulkin Dmitrij Yakovlevich (DE),Шулькин Дмитрий Яковлевич (DE),Rukavishnikova Svetlana Aleksandrovna (RU),Рукавишникова Светлана Алек
申请号：
RU2020110416
公开号：
RU0002733077C1
申请日:
2020.03.11
申请国别(地区):
RU
年份:
2020
代理人:
摘要：
FIELD: medicine.SUBSTANCE: invention refers to medical equipment, namely to a diagnostic technique for acute coronary syndrome. Method involves forming a database containing information on the results of clinical blood analysis of patients with acute coronary syndrome and healthy control group people, which is further used for training neural networks, followed by taking whole patient's blood of the patient being examined, mixing a blood sample, then performing the clinical blood analysis on the automatic hematological analyzer, then the results of the analysis are copied from the analyzer in form of FCS files and transferred to a personal computer for pre-processing and machine analysis, wherein preliminary processing includes transfer by operator using software allowing to work with FCS files, graphic images in the form of patient blood analysis scattergrams into digital equivalent vector, which contains information on all analyzed cells in form of data of their location along axes of scattergram X and Y, wherein the operator, based on the morphological parameters presented for analysis of blood cells differentiates them into three subpopulations: neutrophils, lymphocytes and monocytes, after which the obtained result in digital equivalent is stored in a separate program file for working with electronic tables, then in said digital equivalent of patient blood analysis scattergram – vector, last elements are cut off, namely cells coordinates, so that number of elements of patient being examined corresponds to number of elements of patients, results of which are in a pre-formed database, after which all elements of the vectors of the patient being examined are merged successively into one global vector Vglob, then standardized obtained vector by subtraction from global vector of patient being tested mean value of corresponding vectors from pre-formed database and further division by standard deviation of corresponding database vectors, method then employs a main com
