@inproceedings{ChumachenkoNechyporenkoFrohme2022, author = {Chumachenko, Dmytro and Nechyporenko, Alina and Frohme, Marcus}, title = {Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning}, series = {Proceedings of the 5th International Conference on Informatics \& Data-Driven Medicine, Lyon, France, November 18 - 20, 2022}, volume = {3302}, booktitle = {Proceedings of the 5th International Conference on Informatics \& Data-Driven Medicine, Lyon, France, November 18 - 20, 2022}, editor = {Shakhovska, Natalia and Chr{\´e}tien, St{\´e}phane and Izonin, Ivan and Campos, Jaime}, publisher = {RWTH Aachen}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19074}, pages = {78 -- 85}, year = {2022}, abstract = {The new coronavirus COVID-19 has been spreading worldwide for almost three years. The global community has developed effective measures to contain and control the pandemic. However, new factors are emerging that are driving the dynamics of COVID-19. One of these factors was the escalation of Russia's war in Ukraine. This study aims to test the hypothesis of the influence of migration flows caused by the Russian war in Ukraine on the dynamics of the epidemic process in Germany. For this, a model of the COVID-19 epidemic process was built based on the polynomial regression method. The model's adequacy was tested 30 days before the start of the escalation of the Russian war in Ukraine. To assess the impact of the war on the dynamics of COVID-19, the model was used to calculate the forecast of cumulative new and fatal cases of COVID-19 in Germany in the first 30 days after the start of the escalation of the Russian war in Ukraine. Modeling showed that migration flows from Ukraine are not a critical factor in the growth of the dynamics of the incidence of COVID-19 in Germany, but they influenced the number of cases. The next stage of the study is the development of more complex models for a detailed analysis of population dynamics, identifying factors influencing the epidemic process in the context of the Russian war in Ukraine, and assessing their information content.}, language = {en} } @article{ChumachenkoButkevychLodeetal.2022, author = {Chumachenko, Dmytro and Butkevych, Mykola and Lode, Daniel and Frohme, Marcus and Schmailzl, Kurt J. G. and Nechyporenko, Alina}, title = {Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {18}, publisher = {MDPI}, issn = {1424-8220}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-16521}, year = {2022}, abstract = {Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32\% of the world's population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63\%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks.}, language = {en} } @article{AlekseevaNechyporenkoFrohmeetal.2023, author = {Alekseeva, Victoriia and Nechyporenko, Alina and Frohme, Marcus and Gargin, Vitaliy and Meniailov, Ievgen and Chumachenko, Dmytro}, title = {Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation}, series = {Electronics}, volume = {12}, journal = {Electronics}, number = {5}, publisher = {MDPI}, issn = {2079-9292}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17088}, year = {2023}, abstract = {The share of chronic odontogenic rhinosinusitis is 40\% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09\%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area.}, language = {en} }