2017 № 1 Development of functional tasks and structure of the regional information system for monitoring obstetric Tula region.
Annotation. This paper proposes a way to automate the monitoring of pregnant Tula region taking into account the peculiarities of the organization of obstetric care in the region. Designed for automated functional tasks pregnant monitoring system allows us to implement a three-tier curation pregnant women taking into account the performance of the existing legal and regulatory aspects of the provision of obstetric care. A model of the structure of the monitoring system of obstetrics, automates the process of supervision of pregnant women at all levels of monitoring, automated processes for evaluating and monitoring perinatal risks.
2015 № 1 Predicting the risk of postoperative complications in traumatic injuries of the pancreas.
The article is devoted to the problem of surgical treatment of traumatic injuries of the pancreas. Namely, identify significant risk factors for postoperative complications based on multivariate statistical analysis by logistic regression. The authors compiled prediction model specific postoperative complications with high rates of consent and clinical significance.
2020 № 4 Non-infectious diseases information system for pre-military evaluation of the risk
The article describes a conceptual approach to automating the algorithm for pre-hospital assessment of risk factors
for non-communicable diseases in order to detect diseases early and monitor them later. The presented information system will allow calculating risk factors for non-communicable diseases, providing dynamic monitoring, and creating a unified register of pre-medical examinations. The information system is developed on the basis of a previously developed algorithm for pre-medical assessment of the risk of non-communicable diseases , and allows preliminary identification of risk factors for non-communicable diseases among the General population without conducting expensive analyses and without involving highly qualified medical professionals.
2016 № 5 Analytical processing of databases of the implemented information systems on the example of risk factors of socially dangerous actions of patients with organic damage of a brain.
In article the possibility of use of the existing medical information systems introduced on the basis of medical institutions of the Tyumen region in studying of problems of public health is defined. On the basis of databases of the automated information system “Forensic-psychiatric Examination” the conclusions of out-patient forensic-psychiatric examination of the Tyumen region for 2005–2014 are analysed. The risk factors leading patients with organic damage of a brain to commission of socially dangerous actions are revealed. Are carried to such factors: a male, age till 30 years, the diagnosis “intellectual backwardness”. It is statistically proved that persons with this set of factors commit crimes under 159 and 161 articles of the Criminal code of the Russian Federation. The majority subexpert admit responsible a consequence. In article possible measures for primary prevention of socially dangerous actions of patients with organic damage of a brain are proposed.
2019 № 3 Prospects for the using of machine learning methods for predicting cardiovascular disease
Morbidity and mortality from cardiovascular diseases (CVD) has remained the leading rate in recent decades worldwide. Primary prevention methods based on the management of cardiovascular risk factors are most effective in reducing the burden of CVD. In preventive medicine for risk management of CVD use the riskometers – scales that was obtained as a result of long prospective studies.But the practical application of the developing scales has showed the limitations in the forecast accuracy. Machine learning makes it possible to improve the accuracy of cardiovascular risk prediction due to nonlinear relationships of their deeper adjustment between risk factors and disease outcomes.
2236 patients’ data were used. We trained the model on the features used in the Framingham scale construction. We compared the resulting model and the Framingham scale for the accuracy of the cardiovascular event prediction. Thus, according to the ROC analysis for the Framingham scale, the indicators are as follows: precision Accuracy: 70,0%, the AUC: 0.59. At the same time for the model obtained using machine learning similar indicators were: Accuracy: 78,8%, AUC: 0.84. Thus, the use of machine learning algorithms including deeplearning algorithms can significantly improve the accuracy of cardiovascular risk prediction of trained models.