2020 № 1 Methods of Modelling and Prediction of Surgery Duration
Based on the analysis of foreign publications, a comparative characteristic of methods and models of prediction
of surgical operations duration was carried out. The importance of estimating the accuracy of prediction the duration of
operations for effective planning of the use of operating rooms and high-tech equipment is shown. Analyzed statistical and
regression methods to predict the duration of operations, and the use of artificial neural networks to estimate the duration
of an operation. Mathematical expressions are given, allowing to estimate duration of operation as a whole, as well as data
on prediction errors.
2017 № 3 Reengineering of public health system, based on a person-centered model, hybrid project management approaches and methods of artificial intelligence
The paper considers new approaches of public health emerging on the platform of the technological revolution of information systems. The authors describe the key methods of managerial, technological and mathe- matical interaction with the collective information infrastructure of the healthcare system that will bring qualitative changes in the near future and will form the basis of a digital society, digital economy and public health of a new type. The methods are considered from the point of view of their influence on the formation of new approaches in the process of the economic and social transformation of waves of innovation. The model of the person-centered health care system and the forecast of its impact on the subject area are presented, the advantages of the new model are summarized and the agile project management approaches to its implementation in the current stage of development are presented.
2018 № 2 Decision Support System for Choosing Correction Tactics of Internal Carotid Arteries Stenosis
Along with the medical systems development there is an important task on creation of medical decision support systems (DSS), in particular, capable of predicting the probability of postoperative complications. Computer methods of data analysis make it possible to successfully use both classical methods of applied statistics and modern heuristic procedures for identifying latent (hidden) knowledge in patients’ databases with subsequent construction of predictive models. The article describes the DSS, which automates the prediction of complications probability in the surgical treatment of internal carotid arteries stenoses by carotid endarterectomy and angiostentiation methods basing on the clinical parameters of the patient’s condition before treatment and the technological parameters of the operative intervention. The DSS is based on such classification methods as classification trees and neural networks, the training sample for the classification is the database of patients, who have been treated, with the information of complications presence or absence availiable. Studies, preceding the DSS development were implemented in the STATISTICA package environment. The entrance into program is automated. At the user’s require, the patient data, needed for the calculation is imported into the prediction program module of complications probability from the Excel table. Also, at the user’s request, the prediction results can be saved in the source table.
2019 № 1 Automated system of bacterioscopic diagnosis of tuberculosis
The article describes the scheme of work and requirements for software and hardware complex for automated bacterioscopic diagnosis of tuberculosis. The basic functionality of the hardware of such an automated system and the required capabilities provided by its software are listed. The stages of automated analysis of digital microscopic images of sputum stained by the method of Ziehl-Nielsen are presented. Own algorithms and mathematical models which can be included in such hardware software complex are presented.
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 № itm Forecasting the stage of adenomiosis with neural networks
Adenomyosis is a widespread gynecological disease, which is often accompanied by infertility. There are problems with the diagnosis of the disease, since the disease has various clinical manifestations, including often asymp- tomatic course of the disease. Due to the complexity of the diagnosis and according to different sources, its frequency varies from 5% to 70%. An equally difficult problem is determining the stage of the disease. The stage determines the tactics and strategy of treating patients. A sample of 84 patients with adenomyosis, using the Spearman rank correla- tion coefficient, revealed indicators that are interrelated with the stages of the disease. In this work, the application of the heuristic procedure to neural networks for predicting the laboratory-clinical indicators of the adenomyosis stage is considered. A software application has been developed that allows you to predict the stage of adenomyosis without resorting to hysterectomy. The methodological value of the work is that, using the example of a common gynecological disease, it is shown that the use of modern data analysis tools opens up wide possibilities for solving prognostic prob- lems of determining patients’ belonging to certain classes according to the stages or types of the disease. Software applications that automate the procedure for classifying patients can form the basis of various systems of support for making medical decisions.
2019 № 3 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.
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 № 3 The first Russian breast pathology histologic images data set
Data set of annotated histology images on breast pathology is provided, containing more than 40 thousand images
from 104 microscopic slides and 92 patients and additional clinical data (age, TNM, grade, WHO type). The data set is prepared
in compliance with relevant procedures for clinical research at Burnasyan Federal Medical Biophysical Center Of Federal
Medical Biological Agency. The data set is accessible at GitHub for research and educational purposes.