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1.
Stud Health Technol Inform ; 287: 149-152, 2021 Nov 18.
Article in English | MEDLINE | ID: covidwho-1525369

ABSTRACT

One serious pandemic can nullify years of efforts to extend life expectancy and reduce disability. The coronavirus pandemic has been a perturbing factor that has provided an opportunity to assess not only the effectiveness of health systems for cardio-vascular diseases (CVD), but also their sustainability. The goal of our research is to analyze the influence of public health factors on the mortality from circulatory diseases using machine learning methods. We analysed a very large dataset that consisted of the information collected from the national registers in Russia. We included data from 2015 to 2021. It included 340 factors that characterize organization of healthcare in Russia. The resulting area under receiver operating characteristic curve (AUC of ROC) of the Random Forest based regression model was 92% with a testing dataset. The models allow for automated retraining as time passes and epidemiological and other situations change. They also allow additional characteristics of regions and health care organizations to be added to existing training datasets depending on the target. The developed models allow the calculation of the probability of the target for 6-12 months with an error of 8%. Moreover, the models allow to calculate scenarios and the value of the target indicator when other indicators of the region change.


Subject(s)
Cardiovascular Diseases , Coronavirus Infections , Cardiovascular Diseases/epidemiology , Delivery of Health Care , Humans , Machine Learning , ROC Curve
2.
Stud Health Technol Inform ; 285: 259-264, 2021 Oct 27.
Article in English | MEDLINE | ID: covidwho-1502267

ABSTRACT

Due to the specific circumstances related to the COVID-19 pandemic, many countries have enforced emergency measures such as self-isolation and restriction of movement and assembly, which are also directly affecting the functioning of their respective public health and judicial systems. The goal of this study is to identify the efficiency of the criminal sanctions in Russia that were introduced in the beginning of COVID-19 outbreak using machine learning methods. We have developed a regression model for the fine handed out, using random forest regression and XGBoost regression, and calculated the features importance parameters. We have developed classification models for the remission of the penalty and for setting a sentence using a gradient boosting classifier.


Subject(s)
COVID-19 , Machine Learning , Pandemics , Crime , Humans , Russia/epidemiology
3.
Stud Health Technol Inform ; 273: 223-227, 2020 Sep 04.
Article in English | MEDLINE | ID: covidwho-1256357

ABSTRACT

The current pandemic can likely have several waves and will require a major effort to save lives and provide optimal treatment. The efficient clinical resource planning and efficient treatment require identification of risk groups and specific clinical features of the patients. In this study we develop analyze mortality for COVID19 patients in Russia. We identify comorbidities and risk factors for different groups of patients including cardiovascular diseases and therapy. In the study we used a Russian national COVID registry, that provides sophisticated information about all the COVID-19 patients in Russia. To analyze Features importance for the mortality we have calculated Shapley values for the "mortality" class and ANN hidden layer coefficients for patient lifetime. We calculated the distribution of days spent in hospital before death to show how many days a patient occupies a bed depending on the age and the severity of the disease to allow optimal resource planning and enable age-based risk assessment. Predictors of the days spent in hospital were calculated using Pearson correlation coefficient. Decisions trees were developed to classify the patients into the groups and reveal the lethality factors.


Subject(s)
Coronavirus Infections , Machine Learning , Pandemics , Pneumonia, Viral , Betacoronavirus , COVID-19 , Humans , Russia , SARS-CoV-2 , Survival Analysis
4.
Stud Health Technol Inform ; 273: 262-265, 2020 Sep 04.
Article in English | MEDLINE | ID: covidwho-886152

ABSTRACT

The outbreak of COVID-19 has led to a crucial change in ordinary healthcare approaches. In comparison with emergencies re-allocation of resources for a long period of time is required and the peak utilization of the resources is also hard to predict. Furthermore, the epidemic models do not provide reliable information of the development of the pandemic's development, so it creates a high load on the healthcare systems with unforeseen duration. To predict morbidity of the novel COVID-19, we used records covering the time period from 01-03-2020 to 25-05-2020 and include sophisticated information of the morbidity in Russia. Total of 45238 patients were analyzed. The predictive model was developed as a combination of Holt and Holt-Winter models with Gradient boosting Regression. As we can see from the table 2, the models demonstrated a very good performance on the test data set. The forecast is quite reliable, however, due to the many uncertainties, only a real-world data can prove the correctness of the forecast.


Subject(s)
Coronavirus Infections , Pandemics , Pneumonia, Viral , Betacoronavirus , COVID-19 , Humans , Morbidity , Russia/epidemiology , SARS-CoV-2
5.
Physiol Meas ; 41(10): 10TR01, 2020 11 10.
Article in English | MEDLINE | ID: covidwho-780288

ABSTRACT

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.


Subject(s)
Coronavirus Infections/diagnosis , Coronavirus Infections/physiopathology , Monitoring, Physiologic/methods , Pneumonia, Viral/diagnosis , Pneumonia, Viral/physiopathology , Telemedicine/methods , COVID-19 , Coronavirus Infections/epidemiology , Humans , Pandemics , Pneumonia, Viral/epidemiology
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