2020 № 4 Algorithm for forming a suspicion of a new coronavirus infection based on the analysis of symptoms for use in medical decision support systems
The course of the COVID‑19 pandemic imposes a significant burden on healthcare systems, including on primary care,
when it is necessary to correctly suspect and determine further management. The symptoms non-specificity and the manifestations versatility of the COVID‑19 impose difficulties in identifying suspicions. To improve the definition of COVID‑19 symptom checkers and medical decision support systems (MDSS) can potentially be useful. They can give recommendations for determining the disease management.
The scientific analysis shows the manifestations versatility and the occurrence frequency COVID‑19. We structured the manifestations by occurrence frequency, classified them as “large” and “small”. The rules for their interaction were determined to calculate the level of suspicion for COVID‑19. Recommendations on patient management tactics were developed for each level of suspicion. NLP models were trained to identify the symptoms of COVID‑19 in the unstructured texts of electronic health records. The accuracy of the models on the F-measure metric ranged from 84.6% to 96.0%. Thus, a COVID‑19 prediction method was developed, which can be used in symptom checkers and MDSS to help doctors determine COVID‑19 and support tactical actions.
2018 № 4 Black box problem overcoming in medical applications of machine learning
A new interface for machine learning predicting models is proposed. Approach is based on optimal valid partitioning (OVP) technique and the modified method of statistically weighted syndromes (LSWR). The interface allows you to overcome the problem of “black box” illustrating prediction process by scatter plots, ROC curves and informative indicators ranking
2017 № 2 Determination of risk factors of cardiovascular mortality in institutions of the penitentiary system using the machine learning methods.
The article presents the results of the first clinical and epidemiological studies to identify risk factors for fatal cardiovascular disease in patients of medical institutions of the penitentiary system. In a research the Machine learning methods based on creation of optimal partitioning signs space and recognition methods were applied. It is with high reliability has allowed to determine predictive factors of hospital mortality in cardiac patients, which became: the use of a strong tonic drink «chifir», age, weight, growth, systolic and diastolic blood pressure, hemoglobin level, heart rate, left ventricular ejection fraction, end-systolic and end-diastolic left-ventricular dimension, hypertension and the number of criminal record.
2017 № 3 Prospects for neural networks and deep machine learning in creating health solutions
The paper gives an overview of the prospects of using neural networks and deep machine learning in the creation of artificial intelligence systems for healthcare. The definition and explanations on the technologies of machine learning and neural networks are given. The review of already implemented artificial intelligence projects is presented, as well as the forecast of the most promising directions of development in the near future
2018 № 3 The basic recommendations for the creation and development of information systems in health care based on artificial intelligence
Artificial intelligence is becoming one of the main drivers in solving serious problems of medicine and health, such as inadequate resources, further improving efficiency, quality and speed of work. All over the world, more and more solutions are being developed in this area. However, the more new products appear, the more questions and problems arise.
The work analyzes some foreign publications and research results, which studied the main problems associated with the creation and implementation of artificial intelligence in health care. As a result of the analysis, a number of practical recommendations were formulated that will help increase the likelihood of successful creation and introduction of such products in the practical link of health.
2020 № 4 Predictive analytics technologies in the management of the COVID‑19 pandemic
Recently, a new coronavirus infection, or COVID‑19, caused by the pathogen SARS-CoV‑2, has been continuing to
spread around the world rapidly. According to the World Health Organization (WHO), which declared this outbreak a pandemic, COVID‑19 is a serious public health problem of international concern. Due to the lack of proven effective treatment and vaccination against COVID‑19, precautions are considered by WHO to be strategic goals and a primary response to the pandemic. It is recommended that country guidelines adopt national health care programs aimed at assessing and reducing the risk of infection spread. Predictive analytics have begun to be actively used to compile population and personal forecasts of the progression of morbidity, mortality, assess the severity of the course of the disease, etc. This article provides an overview of available developments and publications on the use of predictive analytics in the management of COVID‑19 pandemic.
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.