Patent RU-2733077-C1 (RU)
Title: Diagnostic technique for acute coronary syndrome
Inventor: PUSHKIN ALEKSANDR SERGEEVICH
IPC: G01N33/48
Filed: 2020-03-11
Granted: 2020-09-29
Abstract: 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 V glob , 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 component method to reduce the dimension of features from n elements to 2–4 main components {Xn}, while preserving as much variability of features as possible through software for mathematical calculations, as a result, a reduced vector is obtained, then performing standardization with the help of described above actions of already reduced vector to type  , after which an ensemble of neural networks is used, which is trained on a pre-formed database of patients with acute coronary syndrome and healthy control group people by introducing information into the computer analysis software on the results of the clinical blood analysis and the final diagnosis – "acute coronary syndrome" or "healthy", wherein the number of neural networks in the ensemble depends on the results of evaluating the prediction accuracy of the final diagnosis and ranges from 1 to 10, wherein algorithms compositions are used, each of which is trained in a subset of a training sample from a pre-formed database of patients with acute coronary syndrome and healthy controls, wherein from a plurality of training samples from 1 to n subsets are selected by random selection of elements with repetitions in each observation subset, then to transfer them to mathematical models for analysis with subsequent aggregation of data in the ensemble and calculating the probability of positive diagnosis, wherein the subsets are considered as representative and independent values of the true data distribution, and on each of the subsets the neural network is trained as a model, wherein the ensemble method is used, and after training neural networks and their aggregation in the ensemble, their accuracy is checked on the test sample, at the same time on the result of training neural networks and the acceptable test result, if the evaluation results error is less than 5 %, similarly performing the clinical examination of the patient's blood, and for assessing the positive diagnosis, a percentage is plotted, where probability fields are used to classify diagnoses in the two-dimensional subspace, which are the abscissa and ordinate axes corresponding to the main components, wherein said fields are considered as vision of neural networks for unknown cases, wherein the patient being diagnosed is diagnosed as follows: if the patient's results are in the area of the schedule allocated for the negative diagnosis, the ensemble of neural networks will give the highest probability for the negative diagnosis, if in the area of the schedule allocated for the positive diagnosis – the highest probability for the positive diagnosis – acute coronary syndrome. 
     EFFECT: technical result is higher accuracy of diagnosis to patient with accuracy of 95–96_77 %. 
     1 cl, 14 dwg, 2 tbl