[Machine Learning Methods in Assessing the Risks of Target Organ Damage in Masked Hypertension].

Aim To develop models for predicting the risk of target organs damage (TOD) in different phenotypes of "masked" arterial hypertension (MAH) based on methods of machine learning (ML).Material and methods A retrospective cohort analysis was performed for 284 clinical records of patients (261 males, 23 females; median age, 38 years). Group 1 included 125 patients with grade 1-2 arterial hypertension (AH) and low or moderate risk; group 2 included 159 subjects with normal "office" blood pressure (BP) exposed to chronic professional stress. The 24-h BP monitoring (24-h BPM) and ultrasound examination of the heart and carotid arteries were performed; glomerular filtration rate (GFR) was estimated using the СКD-EPI formula. MAH was phenotyped by clustering 24-h BPM data, and the risk of TOD was predicted by analysis of odd ratios (OR) and with the ML methods, random forest (RF) and artificial neural networks (ANN). Data were analyzed using the R language in the RStudio environment.Results According to results of the 24-h BPM and cluster analysis, 121 (76.1 %) subjects of group 2 had MAH. The MAH phenotypes were identified as follows: systolic-diastolic (SDMAH) (43.8 %); isolated systolic (ISMAH) (35.5 %), and isolated diastolic (IDMAH) (20.7%). As compared to stable AH, subjects with different MAH phenotypes showed both increases and decreases in individual 24-h BPM indexes. Thus, in subjects with IDMAH, mean 24-h values of systolic and diastolic BP were significantly lower than with AH while in SDMAH, they were considerably higher. The OR analysis demonstrated that odds of differently located TOD were associated with definite MAH phenotypes. With that, ISMAH was associated with the highest risk of glomerular hyperfiltration; IDMAH was associated with reduced GFR and vascular remodeling; and SDMAH was associated with left ventricular myocardial hypertrophy. The developed models for predicting the risk of TOD based on the RF and ANN methods showed a high accuracy, which was provided by multistep procedures of selecting the predictors and cross-validation.Conclusion Modern ML technologies enhance the risk stratification of patients with different clinical variants of AH.

[The Analysis of Social Economic Factors Impact on Epidemic Situation with Tuberculosis in Regions of the Russian Federation].

The article presents original multi-factorial linear regression models developed on the basis of panel structure of data. The distribution of the regions of the Russian Federation according tuberculosis burden was implemented using cluster analysis technique. The evaluation of degree of impact of analyzed factors to epidemic process was implemented using Pratt metric. In overall, approximately 100,000 records from 78 regions of the Russian Federation were processed. four regional clusters were marked out differentiating by level of morbidity, prevalence and mortality of tuberculosis. The diversity of social economic factors impacting the main indices of epidemic process is reducing as its intensity increases. If in the first cluster 7 factors impacted on morbidity and 4 factors -- on mortality, then in the fourth cluster only 1 factor impacted morbidity and 2 -- on mortality. At that, intensity of their impact on epidemic process increased significantly. So, for example, the role of such factor as "Area of housing per capita" in decreasing of tuberculosis morbidity among regions of the 4th cluster assessed in 79%, and among regions of 1st cluster only in 17%. The study results demonstrated that modern techniques of machine learning permit to develop models for quantification assessing of impact of social economic conditions of a particular region on activity of epidemic process of tuberculosis.