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Clinical search engines development is actual task for medical informatics. The main issue in this area is to implement high-quality unstructured texts processing. Ontological interdisciplinary metathesaurus UMLS can be used to solve this problem. Currently, there is no unified method to relevant information aggregation from UMLS. In this research, we have presented the UMLS as graph model and performed the spot check of UMLS structure to identify basic problems. Then we created and integrated new graph metric in two created by us program modules for relevant knowledge aggregation from UMLS.

Aim. To establish risk factors for heart failure (HF) in patients with coronavirus disease 2019 (COVID-19).Material and methods. Medical records of 151 patients treated in an infectious disease hospital from November 3, 2020 to February 2, 2021 with a confirmed diagnosis of COVID-19 were retrospectively selected. The collection of clinical, history and laboratory data were carried out by analyzing electronic medical records. We analyzed information on age, sex, body mass index, smoking, and comorbidities. Following laboratory studies were analyzed: complete blood count, biochemical blood tests, coagulation profile, acute phase proteins (C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH)), procalcitonin. The diagnosis of HF was confirmed by clinical performance, echocardiography, and elevated levels of the N-terminal pro-brain natriuretic peptide (NT-proBNP). The risk of HF was taken as the endpoint of the study.Results. The studied sample of patients was divided into two groups depending on HF: the 1st group included 46 patients with HF, the 2nd group — 105 patients without HF. The median age was 66,2 (50-92) years (women, 91 (60,3%)). Laboratory indicators, such as the levels of CRP, LDH, procalcitonin, creatinine, bilirubin, differed significantly from each other, and the median values were higher in patients with HF. The neutrophil-to-lymphocyte ratio (NLR) showed significant intergroup differences: in the group of patients with HF, the median was 4,97% vs 3,62% (p=0,011) in the group of patients without HF. There were following most significant predictors increasing the HF risk: age ≥66 years (odds ratio, 8,038, p<0,001), procalcitonin level, which increases the HF risk in patients by 3,8 times (p><0,001), NLR ≥4,11% (p=0,010), thrombocytopenia ≤220×109/l (p=0,010), history of chronic kidney disease (CKD) (p=0,018). Conclusion. The following predictors of HF were established: age ≥66 years, procalcitonin ≥0,09 ng/ml, NLR ≥4,11%, thrombocytopenia ≤220×109/l, history of CKD, LDH ≥685 U/l and creatinine ≥102 µmol/l, international normalized ratio ≥1,19, QTc interval ≥407,5 ms, bilirubin ≤10,7 µmol/l. It is worth noting that the best accuracy values are demonstrated by the Random Forest algorithm (88,5% on the validation set), but the mathematical model of the neural network turned out to be the most sensitive (90,0% on the validation set). Keywords: novel coronavirus infection, heart failure, prognosis>˂0,001), procalcitonin level, which increases the HF risk in patients by 3,8 times (p˂0,001), NLR ≥4,11% (p=0,010), thrombocytopenia ≤220×109/l (p=0,010), history of chronic kidney disease (CKD) (p=0,018).Conclusion. The following predictors of HF were established: age ≥66 years, procalcitonin ≥0,09 ng/ml, NLR ≥4,11%, thrombocytopenia ≤220×109/l, history of CKD, LDH ≥685 U/l and creatinine ≥102 µmol/l, international normalized ratio ≥1,19, QTc interval ≥407,5 ms, bilirubin ≤10,7 µmol/l. It is worth noting that the best accuracy values are demonstrated by the Random Forest algorithm (88,5% on the validation set), but the mathematical model of the neural network turned out to be the most sensitive (90,0% on the validation set).

The respiratory pump that provides pulmonary ventilation includes the respiratory center, peripheral nervous system, chest and respiratory muscles.The aim of this study was to evaluate the activity of the respiratory center and the respiratory muscles strength after COVID-19 (COronaVIrus Disease 2019).Methods. The observational retrospective cross-sectional study included 74 post-COVID-19 patients (56 (76%) men, median age – 48 years). Spirometry, body plethysmography, measurement of lung diffusing capacity (DLCO), maximal inspiratory and expiratory pressures (MIP and MEP), and airway occlusion pressure after 0.1 sec (P0.1) were performed. In addition, dyspnea was assessed in 31 patients using the mMRC scale and muscle strength was assessed in 27 of those patients using MRC Weakness scale.Results. The median time from the COVID-19 onset to pulmonary function tests (PFTs) was 120 days. The total sample was divided into 2 subgroups: 1 – P0.1 ≤ 0.15 kPa (norm), 2 – > 0.15 kPa. The lung volumes, airway resistance, MIP, and MEP were within normal values in most patients, whereas DLCO was reduced in 59% of cases in both the total sample and the subgroups. Mild dyspnea and a slight decrease in muscle strength were also detected. Statistically significant differences between the subgroups were found in the lung volumes (lower) and airway resistance (higher) in subgroup 2. Correlation analysis revealed moderate negative correlations between P0.1 and ventilation parameters.Conclusion. Measurement of P0.1 is a simple and non-invasive method for assessing pulmonary function. In our study, an increase in P0.1 was detected in 45% of post-COVID-19 cases, possibly due to impaired pulmonary mechanics despite the preserved pulmonary ventilation as well as normal MIP and MEP values.

Introduction. The creation of safe working conditions allows not only to reduce the rates of occupational morbidity and occupational injuries among employees, but also to increase the efficiency and efficiency of work, which together reduce the financial losses of employers. Therefore, the improvement of methodological approaches to the assessment of occupational risks is an urgent task of modern preventive medicine. The study aims to substantiate the need to apply new approaches and criteria for determining the level of occupational risk based on the analysis of occupational morbidity in a group of people exposed to harmful industrial factors. Materials and methods. Researchers have carried out a retrospective analysis of occupational morbidity and an analysis of the working conditions of railway transport workers (798,126 people) operating in conditions of constant exposure to harmful production factors. Indicators of occupational morbidity have been recalculated for 10 thousand workers employed in workplaces with harmful and dangerous working conditions. The authors used the software and application package "SPSS 23" during the calculations. Results. The main occupational diseases of railway transport workers are: professional sensorineural hearing loss (PSHL) (60-75%), pneumoconiosis and dust bronchitis (4-20%), diseases of the peripheral nervous system and musculoskeletal system (6-9%), vibration disease (WD) (3-10%). The indicator of occupational morbidity per 10.000 of all employees of JSC "Russian Railways" for PNST, radiculopathy and vibration disease were 0.25, 0.014, 0.056, respectively. The new indicator of occupational morbidity, calculated for 10,000 workers working in harmful and dangerous working conditions with factors such as noise, vibration, and severity of labor, was: PSHL - 1.08, radiculopathy - 0.06, WD - 0.78. We have carried out the forecasting of the development of PSHL, which is based on data on the profession, the presence of harmful production factors, age-experience indicators and the degree of PSHL. Conclusion. The existing approach to the assessment of occupational morbidity, calculated for 10 thousand of all employees, is not objective, does not reflect real risk levels, understates official data on occupational morbidity, and creates difficulties for the development of effective management decisions. It is necessary to approve a new approach to the assessment of occupational morbidity, calculating it for 10 thousand workers directly exposed to harmful and dangerous factors. The developed prognostic model of the risk of developing PSHL, based only on information about the profession, work experience, and age of the employee, is not objective and does not give accurate predictions about the timing of the onset of occupational disease. To improve the prognostic model, it is necessary to include additional indicators based on clinical and laboratory studies, as provided by the methodology for the formation of risk groups (groups 1-5) for the development of occupational diseases.