9 research outputs found
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 , sensitivity of
, specificity of and precision of
, showing promising segmentation results.Comment: 18 pages, 4 figures, 2 table
ΠΠ½Π°Π»ΠΈΠ· ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΠΈ ΡΠΈΠΏΠΎΠ»ΠΎΠ³ΠΈΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² ΠΏΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΡΠ°ΠΌΠΎΡΡΠΎΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ
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 Federationβs 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 Β«advancedΒ» and Β«backwardΒ» 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.Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠ΅ΠΆΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΡΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΠΉ ΠΈ ΠΊΠ»Π°ΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΡ
Ρ ΡΠ΅Π»ΡΡ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΡΡΡΠΎΠΉΡΠΈΠ²ΡΡ
Π³ΡΡΠΏΠΏ ΡΡΠ±ΡΠ΅ΠΊΡΠΎΠ² Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ ΡΡΠΎΠ²Π½ΡΠΌ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ, ΠΎΡΡΠ°ΠΆΠ°ΡΡΠΈΡ
Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΈ ΡΠ°Π·ΠΌΠ΅ΡΡ Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π°, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΠ»ΠΎΠ²ΠΈΡ Π΅Π³ΠΎ ΠΌΠΎΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠΈ Π½Π° ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ. ΠΠ½Π°Π»ΠΈΠ· ΠΎΡΠ½ΠΎΠ²Π°Π½ Π½Π° ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ, ΠΎΡΡΠ°ΠΆΠ°ΡΡΠΈΡ
Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΡΡΠ±ΡΠ΅ΠΊΡΠ° Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ Π² Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΡΠ°ΠΌΠΎΡΡΠΎΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Ρ ΡΠΎΡΠΊΠΈ Π·ΡΠ΅Π½ΠΈΡ Π½Π°Π»ΠΈΡΠΈΡ ΠΈ ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ² ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° Π½Π° ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Π°, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π²ΡΠΈΡ
ΡΡ ΡΡΠ»ΠΎΠ²ΠΈΠΉ, Π² ΠΊΠΎΡΠΎΡΡΡ
ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ ΠΌΠΎΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΡ Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° Π² ΡΠΎΡΠΌΠ΅ Π½Π°Π»ΠΎΠ³ΠΎΠ²ΡΡ
ΠΏΠΎΡΡΡΠΏΠ»Π΅Π½ΠΈΠΉ ΠΈ Π΅Π³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅. ΠΠ»Ρ ΡΡΠΎΠΉ ΡΠ΅Π»ΠΈ Π² ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ ΡΠ°ΠΊΠΈΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ, ΠΊΠ°ΠΊ ΡΡΠΎΠ²Π½ΠΈ ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠΈ Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π°; ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠΎΠ² Π½Π°Π»ΠΎΠ³ΠΎΠ²ΡΡ
ΡΠ΅ΡΡΡΡΠΎΠ²; ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΡ Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΠΈ Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° (ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠ΅ΠΉ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ Π±ΡΠ΄ΠΆΠ΅ΡΠ°) ΠΈ ΠΏΠΎΠ»Π½ΠΎΡΡ Π΅Π³ΠΎ ΠΌΠΎΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠΈ. Π’Π°ΠΊΠΆΠ΅ Ρ ΡΠ΅Π»ΡΡ ΠΎΡΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ² ΡΠ°Π·Π²ΠΈΡΠΈΡ Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΡΠ΅Π³ΠΈΠΎΠ½Π° Π² ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠΉ Π½Π°Π³ΡΡΠ·ΠΊΠΈ ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ. ΠΠ½Π°Π»ΠΈΠ· ΡΡΠ΄ΠΎΠ² ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² ΠΏΠΎ ΠΈΠ·ΡΡΠ°Π΅ΠΌΡΠΌ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌ (ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ ΠΏΠΎ Π΄Π°Π½Π½ΡΠΌ Π·Π° 2006-2013 Π³Π³.) Π²ΡΡΠ²ΠΈΠ» Π½Π΅ΠΈΠ·ΠΌΠ΅Π½Π½ΠΎΡΡΡ ΡΠΈΡΡΠ°ΡΠΈΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ Π½Π΅ΡΠ°Π²Π΅Π½ΡΡΠ²Π°, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΡ ΠΏΠΎΠ·ΠΈΡΠΈΠΉ ΡΡΠ±ΡΠ΅ΠΊΡΠΎΠ² Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ Π΄ΡΡΠ³ ΠΊ Π΄ΡΡΠ³Ρ. ΠΡΠΎ ΠΎΠ±ΡΡΠ»ΠΎΠ²ΠΈΠ»ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΡΠΈΠΏΠΎΠ»ΠΎΠ³ΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠ±ΡΠ΅ΠΊΡΠΎΠ² Π Π€ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Ρ ΠΏΠΎΠ·ΠΈΡΠΈΠΈ Β«ΠΏΠ΅ΡΠ΅Π΄ΠΎΠ²ΡΡ
Β» ΠΈ Β«ΠΎΡΡΡΠ°ΡΡΠΈΡ
Β» ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ², Π½ΠΎ ΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ², ΠΎΡΡΠ°ΠΆΠ°ΡΡΠΈΡ
ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΠΈ ΡΡΠ»ΠΎΠ²ΠΈΠΉ Π΅Π³ΠΎ ΠΌΠΎΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠΈ. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ Π²ΡΠ΄Π΅Π»Π΅Π½Ρ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΠ΅, ΡΡΡΠΎΠΉΡΠΈΠ²ΡΠ΅ Π²ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΡΠΈΠΏΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π³ΡΡΠΏΠΏΡ ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ², ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΠΈΡ
ΠΎΡΠ»ΠΈΡΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ
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