CONTENT OF THE ISSUE
Continuous electronic monitoring movement of drugs from the manufacturer to the end user
The paper describes the approaches and methods, used in the experiment on the implementation of the Federal state
system for electronic monitoring the movement of drugs on the basis of a large region (Novosibirsk region), the regulatory framework,the analysis of the results of the experiment. The features of the process of implementation of the results are formulated and generalized, conclusions about the current efficiency of the experiment and measures to improve it are made.
The use of a clinical information system for patients with circulatory system diseases selection to supply them a high-tech medical care
Clinical Institute named after M. F. Vladimirsky», Moscow, Russia)
Abstract. The manuscript presents results of the clinical information system (CIS) application for ensuring the selection of patients with circulatory system diseases for receiving them of high-tech medical care.
The using of CIS is based on the level of morbidity, hospitalization, mortality of the population analysis at Moscow region. CIS provide specialists the information about comparative analysis of population health indicators to determine the needs of municipal
citizens in specific types of high-tech medical care in relevant medical organizations.
Also CIS can be used for monitoring the selection of patients for high-tech medical care, assessing the quality and effectiveness of this type of medical care in various medical organizations. Measures in public health conducted at Moscow region led to decreasing the death rate of patients with circulatory system diseases (CSD). This rate has decreased from 676.2 per 100 thousand population in 2015, to 475.2 per 100 thousand in 2018.
We should pay attention that such data demonstrates the lower mortality from CSD than the average in the Russian Federation and the Central Federal District in recent years.
About the classification of risks of application of the medical software in the Eurasian economic union
The rules of classification of risks of medical software – Software as a Medical Device (SaMD), adopted by the International forum of regulators of medical devices (IMDRF), the Eurasian economic Union (EAEU) and the European Union (EU). Formal models of these rules are described. A comparative analysis of the risk classes of SaMD is done, examples of which are described in the documents IMDRF, defined by these rules.
Development of algorithm for searching of clinically homogeneous patients from semistructured text data of oncological electronic health record
The growth in the number of patients with malignant neoplasms in Russia significantly increases the load on a specialized network of oncological institutions and oncologists. It is most likely that this trend will continue in the coming years. One of the ways to improve the efficiency of medical activity is the extraction knowledge from medical data arrays, using modern data analysis methods, by clustering patients into groups of clinically homogeneous (similar) patients from electronic health records. The aim of the study is to develop an algorithm for finding clinically homogeneous patients according to the electronic health records of the oncological dispensary, with follow-up possibility of integration into the clinical decision support system (CDSS). The use of such CDSS in practical medicine and in the field of medical education will allow us to analyze both semistructured and unstructured arrays of information, which will require further implementation and improvement of information systems at all levels of
medical care. The homogeneity of patients was determined by machine learning by cosine distance in the space of vector representations of electronic health records. An experiment on 20 randomly selected electronic health records of patients of Krasnodar Regional Oncological Dispensary showed high efficiency of the algorithm in creating clusters of clinically homogeneous patients.
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.
Experience in developing and implementing an oncological formations searching system using artificial intelligence with the example of X‑ray computed tomography of the lungs
The experience of creating and implementing AI system Botkin.AI for identifying pulmonary nodules according to CT
data is considered. The main parameters of the model are described, the results of pilot projects of platform practical application in several regions of the Russian Federation are presented. Examples of a platform application for identifying pulmonary nodules with various size and localization are given. During pilot projects in the regions, 7 patients with high suspicion of pulmonary malignancy were identified. The results and experience show that the applying of the Botkin.AI system can be used both for the implementation of regional lung cancer screening programs and as an additional tool to increase the lung cancer detections with introducing automatic revision of chest CT data, regardless of these studies indications.
The use of blockchain-technology for maintaining the State register of medicines
The use a closed blockchain system in electronic document flow and information exchange of data on biologically
active compounds as a basic information process, including all phases of the life cycle of the drug, the object of the sphere of circulation of medicines. The main advantages of this technological approach to the procedures of maintaining the State register of medicines in terms of monitoring of registration information and subsequent control in the procurement and distribution of medicines for state and municipal needs are described.
Сloud decision support service for diagnosis in gastroenterology
The decision support service for diagnosis in gastroenterology was developed on a medical portal of the IACPaaS cloud platform. The general principles of development and the concept architecture of intelligent service, developed information and software components are described. The possibilities of diagnosis and differential diagnosis of diseases on the medical portal are presented.
Medical Informatics in modern higher medical education
The article is about the problems of teaching medical Informatics in accordance with the changing requirements of
standards of higher medical education and the labor market. The changes in the teaching of medical Informatics over the past 20 years are considered. A draft work program for this discipline is proposed that takes into account current trends in healthcare informatization.
Regional informatizatian projects
Medical information systems
Artificial intelligence in health care
Blockchain in Healthcare
Decision support systems
Information technologies in education