title: Prediction of the fact and degree of coronary artery disease using the processing of clinical and instrumental data by artificial intelligence
metadata.name: dc.contributor.author
metadata.value: Abdualimov, Timur P.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Obrezan, Andrey G.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2022-03-09T19:57:01Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2022-03-09T19:57:01Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2021-09
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Abdualimov T. P., Obrezan A. G. Prediction of the fact and degree of coronary artery disease using the processing of clinical and instrumental data by artificial intelligence. Vestnik of Saint Petersburg University. Medicine, 2021, vol. 16, issue 3, pp. 153–158.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu11.2021.302
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/35579
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: Aim of the study was to analyze the possibility of using neural network analysis to predict the
  severity of coronary bed lesion. The study was also designated to determine the performance
  and accuracy of the trained neural network model receiving input as the structured data and
  EGG images with the parameters and leads positioning differ from the training sample, and
  also to compare the efficiency of detecting transient myocardial ischemia with traditional diagnostic
  methods, such as 24-hour Holter monitoring, treadmill test. Neural network analysis
  of the available clinical, laboratory and instrumentation data allow to configure the network
  parameters for further prediction of coronary artery disease. The results obtained in the form
  of an AUC score allow to consider this method to be effective in the coronary artery disease
  diagnosis using recorded ECG tape with parameters and lead positioning differ from initial
  training sample. The efficiency of transient myocardial ischemia detection on the training
  sample of the trained neural network is higher in comparison with traditional diagnostic
  methods, such as 24-hour Holter monitoring, treadmill test.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Medicine;Volume 16; Issue 3
metadata.lang: -
metadata.name: dc.subject
metadata.value: coronary arteries
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: neural networks
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: deep learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: ECG
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Prediction of the fact and degree of coronary artery disease using the processing of clinical and instrumental data by artificial intelligence
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 153-158.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/35579/1/153-158.pdf
resources.format: Adobe PDF
resources.size: 567,65 kB