• 2016 № 2 Telemedicine prevention, rehabilitation and health management: problems and solutions

    The analysis of the problems arising when introducing technologies of personal telemedicine in the practice healthcare is performed. The ways to overcome obstacles and improve the effectiveness of telemedicine technologies for the prevention and rehabilitation of cardiovascular diseases are suggested.

    Authors: Yu. Yu. Kudryashov [1] O. Yu. Atkov [1] Kasimov. O. V. [1]

    Tags: medical decision support system2 personal telemedicine1 rehabilitation and prevention1

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  • Decision support systems
  • 2018 № 2 Decision Support System for Choosing Correction Tactics of Internal Carotid Arteries Stenosis

    Along with the medical systems development there is an important task on creation of medical decision support systems (DSS), in particular, capable of predicting the probability of postoperative complications. Computer methods of data analysis make it possible to successfully use both classical methods of applied statistics and modern heuristic procedures for identifying latent (hidden) knowledge in patients’ databases with subsequent construction of predictive models. The article describes the DSS, which automates the prediction of complications probability in the surgical treatment of internal carotid arteries stenoses by carotid endarterectomy and angiostentiation methods basing on the clinical parameters of the patient’s condition before treatment and the technological parameters of the operative intervention. The DSS is based on such classification methods as classification trees and neural networks, the training sample for the classification is the database of patients, who have been treated, with the information of complications presence or absence availiable. Studies, preceding the DSS development were implemented in the STATISTICA package environment. The entrance into program is automated. At the user’s require, the patient data, needed for the calculation is imported into the prediction program module of complications probability from the Excel table. Also, at the user’s request, the prediction results can be saved in the source table.

    Authors: Koshkarov A. A. [4] Khalafyan A. A. [1] Vinogradov R. A. [1] Akin’shina V. A. [1]

    Tags: angiostenting1 carotid endarterectomy1 classification trees1 medical decision support system2 neural networks3

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