Artificial intelligence in diabetology

This review presents the applications of artificial intelligence for the study of the mechanisms of diabetes development and generation of new technologies of its prevention, monitoring and treatment. In recent years, a huge amount of molecular data has been accumulated, revealing the pathogenic mechanisms of diabetes and its complications. Data mining and text mining open up new possibilities for processing this information. Analysis of gene networks makes it possible to identify molecular interactions that are important for the development of diabetes and its complications, as well as to identify new targeted molecules. Based on the big data analysis and machine learning, new platforms have been created for prediction and screening of diabetes, diabetic retinopathy, chronic kidney disease, and cardiovascular disease. Machine learning algorithms are applied for personalized prediction of glucose trends, in the closed-loop insulin delivery systems and decision support systems for lifestyle modification and diabetes treatment. The use of artificial intelligence for the analysis of large databases, registers, and real-world evidence studies seems to be promising. The introduction of artificial intelligence systems is in line with global trends in modern medicine, including the transition to digital and distant technologies, personification of treatment, high-precision forecasting and patient-centered care. There is an urgent need for further research in this field, with an assessment of the clinical effectiveness and economic feasibility

Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks

In this paper, an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images is proposed. Our deep-learning approach is based on the popular 3D U-Net convolutional neural network architecture, which was modified by adding the squeeze-and-excitation blocks and residual connections. Robust pre-processing methods were implemented to improve the segmentation accuracy. Moreover, a specific patches sampling strategy was used to address the large size of medical images, to smooth out the effect of the class imbalance problem and to stabilize neural network training. All experiments were performed using five-fold cross-validation on the dataset containing non-contrast computed tomography volumetric brain scans of 81 patients diagnosed with acute ischemic stroke. Two radiology experts manually segmented images independently and then verified the labeling results for inconsistencies. The quantitative results of the proposed algorithm and obtained segmentation were measured by the Dice similarity coefficient, sensitivity, specificity and precision metrics. Our proposed model achieves an average Dice of $0.628\pm0.033$, sensitivity of $0.699\pm0.039$, specificity of $0.9965\pm0.0016$ and precision of $0.619\pm0.036$, showing promising segmentation results.Comment: 18 pages, 4 figures, 2 table

Анализ устойчивости типологии российских регионов по показателям возможностей достижения финансовой самостоятельности

The article presents the results of the inter-regional comparisons and cluster analysis performed to identify stable groups of subjects of the Russian Federation, accordingly to the levels of indicators that reflect the presence and size of the tax capacity, as well as the conditions for its mobilization in the territory. The analysis is based on the proposed system of indicators reflecting the possibilities of the Russian Federation regions to achieve financial self-sufficiency in terms of the presence and size of the elements of the tax potential in the region and creation conditions in which there is a mobilization of tax potential in the form of tax revenues, and its development. For this purpose, a system of indicators includes features such as: the level of actual tax mobilization capacity; indicators of components of tax resources; specifications of adequacy of tax capacity (respecting to needs of regional budget) and completeness of its mobilization. Also, with a purpose to reflect the prospects of development of tax potential of region, there are included in the system characteristics of the tax burden and investment activity. Analysis of the distribution series of regions on the studied characteristics (the study was conducted on data based for period 2006-2013 years) showed the immutability of the situation of regional disparities, as well as the stability of the position of the Russian Federation’s regions (subjects) in relation to each other. This led to necessity of typologization of the federal subjects of Russia, not only from a position of «advanced» and «backward» regions, but also with respect to the parameters that reflect the qualitative features of regional tax capacity and conditions for its mobilization. As a result, there was highlighted some stable in time typological groups of regions and given their distinctive characteristics.В статье представлены результаты межрегиональных сопоставлений и кластерного анализа, проведенных с целью выявления устойчивых групп субъектов Российской Федерации соответственно уровням показателей, отражающих наличие и размеры налогового потенциала, а также условия его мобилизации на территории. Анализ основан на предложенной системе показателей, отражающих возможности субъекта Российской Федерации в достижении финансовой самостоятельности с точки зрения наличия и размеров элементов налогового потенциала на территории региона, а также сформировавшихся условий, в которых осуществляется мобилизация налогового потенциала в форме налоговых поступлений и его развитие. Для этой цели в систему показателей включены такие характеристики, как уровни фактической мобилизации налогового потенциала; показатели компонентов налоговых ресурсов; индикаторы достаточности налогового потенциала (относительно потребностей регионального бюджета) и полноты его мобилизации. Также с целью отражения перспектив развития налогового потенциала региона в систему показателей включены характеристики налоговой нагрузки и инвестиционной активности. Анализ рядов распределения регионов по изучаемым характеристикам (исследование проводилось по данным за 2006-2013 гг.) выявил неизменность ситуации регионального неравенства, а также устойчивость позиций субъектов Российской Федерации по отношению друг к другу. Это обусловило необходимость проведения типологизации субъектов РФ не только с позиции «передовых» и «отстающих» регионов, но и на основе параметров, отражающих качественные особенности регионального налогового потенциала и условий его мобилизации. В результате выделены отдельные, устойчивые во времени типологические группы регионов, приведены их отличительные характеристики

Machine Learning Models for Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes

Nocturnal hypoglycemia (NH) is a dangerous complication of insulin therapy that often goes undetected. In this study, we aimed to generate machine learning (ML)-based models for short-term NH prediction in hospitalized patients with type 1 diabetes (T1D). The models were trained on continuous glucose monitoring (CGM) data obtained from 406 adult patients admitted to a tertiary referral hospital. Eight CGM-derived metrics of glycemic control and glucose variability were included in the models. Combinations of CGM and clinical data (23 parameters) were also assessed. Random Forest (RF), Logistic Linear Regression with Lasso regularization, and Artificial Neuron Networks algorithms were applied. In our models, RF provided the best prediction accuracy with 15 min and 30 min prediction horizons. The addition of clinical parameters slightly improved the prediction accuracy of most models, whereas oversampling and undersampling procedures did not have significant effects. The areas under the curve of the best models based on CGM and clinical data with 15 min and 30 min prediction horizons were 0.97 and 0.942, respectively. Basal insulin dose, diabetes duration, proteinuria, and HbA1c were the most important clinical predictors of NH assessed by RF. In conclusion, ML is a promising approach to personalized prediction of NH in hospitalized patients with T1D