ACADEMIC ACTIVITY Academic activity of the department aims at providing the necessary knowledge and practical skills of information technology usage in professional medical activities, mastering the main methods of Information and Computer Technologies application in medicine, processing and analysis medical data, studying Data Science methods and Data Mining techniques application in medicine, managing the principles of Databases and Expert Systems, researching Internet medical resources and student knowledge monitoring. MAJOR TRENDS OF SCIENTIFIC RESEARCH The general topic of the scientific research at the department during the period since 2000 was “Creation of medical and biological data models”. Since 2008 the “Biosignal analysis of Arterial oscilography and ECG signals” has been added. Other areas of scientific research activities included: General research areas of activity in Biostatistics Developing the methods and tools for processing of biomedical data, including simulation algorithms for analysis Modeling the biomedical processes. Clinical Predictive Modeling. Developing the elements of medical information and telecommunication systems. Software solutions to facilitate distance education processes. Curriculum development for distance education learning. Main reference Martsenyuk VP, Vakulenko DV. On model of interaction of cell elements at bone tissue remodeling. J Autom Inform Sci 2007;39(3):68-80., doi: 10.1615/JAutomatInfScien.v39.i3.70. Martsenyuk V, Vakulenko D, Vakulenko L, Kłos-Witkowska A, Kutakova O. Information System of Arterial Oscillography for Primary Diagnostics of Cardiovascular Diseases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 17th International Conference, CISIM 2018; 2018 Sep 27-29; Olomouc, Czech Republic. Berlin: Springer; 2018: Р. 46-56. Selskyy Petro, Vakulenko Dmytro, Televiak Anatolii, Veresiuk Taras. On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering. Family Medicine & Primary Care Review 2018; 20(2): 171–175. https://doi.org/10.5114/fmpcr.2018.76463. Dmytro V. Vakulenko, Vasyl P. Martseniuk, Liudmyla O. Vakulenko, Petro R. Selskyi, Oksana V. Kutakova, Olena V. Gevko, Taras B. Kadobnyj (2019) Cardiovascular system adaptability to exercise according to morphological, temporal, spectral and correlation analysis of oscillograms / Family Medicine & Primary Care Review 2019; 21(3): 253–263. DOI: https://doi.org/10.5114/fmpcr.2019.88385 Mintser O., Martsenyuk V., Vakulenko D. (2020) On Data Mining Technique for Differential Diagnostics Based on Data of Arterial Oscillography. In: Zawiślak S., Rysiński J. (eds) Engineer of the XXI Century. Mechanisms and Machine Science, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-13321-4_23 Martsenyuk V.P., Vakulenko D.V., Skochylyas S.M., Vakulenko L.O. (2020) Modeling and Stability Investigation of Investment of Health Sector on Regional Level. In: Wilimowska Z., Borzemski L., Świątek J. (eds) Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019. ISAT 2019. Advances in Intelligent Systems and Computing, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-30443-0_11 Martsenyuk, V.P., Vakulenko, D.V., Hryshchuk, L.A., Vakulenko, L.O., Kravets, N.O., Klymuk, N.Y. On the Development of Directed Acyclic Graphs in Differential Diagnostics of Pulmonary Diseases with the Help of Arterial Oscillogram Assessment (2022) Mechanisms and Machine Science, 107, pp. 157-173. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112425856&doi=10.1007%2f978-3-030-76787-7_8&partnerID=40&md5=f4b7a9fc22db55af66b65fcb0ce5d05e DOI: 10.1007/978-3-030-76787-7_8 Dmytro Vakulenko, Liudmyla Vakulenko, Leonid Hryshchuk and Lesya Sas Aplication Arterial Oscilography to Study the Adaptive Capacity of Subject with COVID-19 in Primary Care Submitted: May 10th 2021Reviewed: May 25th 2021Published: August 19th 2021 DOI: 10.5772/intechopen.98570 https://www.intechopen.com/online-first/77590 Dmytro Vakulenko, Hryhoriy Zaspa, Sergiy Lupenko. New Application of Blood Pressure Monitor with Software Environment Oranta-AO based on Arterial Oscillography Methods (2021) IDDM 2021 Informatics & Data-Driven Medicine. Proceedings of the 4th International Conference on Informatics & Data-Driven Medicine Valencia, Spain, November 19 - 21, 2021. 161-171. http://ceur-ws.org/Vol-3038/paper11.pdf Semenets, A., & Vakulenko, D. (2021). A year of the COVID-19 Lockdown: comparative analysis of Distance Learning approaches in TNMU. In Manuel Filipe Pereira da Cunha Martins Costa & José Benito Vázquez Dorrío (Eds.), Hands-on Science. Science Education. Discovering and understanding the wonders of Nature. Hands-on Science Network, 2021 (pp. 106–110). Retrieved from https://repository.tdmu.edu.ua//handle/123456789/17097 Semenets, A. V, VAKULENKO, D. V, & Berezovska, I. (2020). Education during the COVID-19 Lockdown: Does the Pandemic Extend the Scope of Distance Learning? In M. Costa & B. Dorrío (Eds.), Hands-on Science. Science Education. Discovering and understanding the wonders of Nature. Hands-on Science Network, 2020. Retrieved from https://repository.tdmu.edu.ua/handle/123456789/16957 Martsenyuk, V., Povoroznyuk, V., Semenets, A., & Martynyuk, L. (2019). On an approach of the solution of machine learning problems integrated with data from the open-source system of electronic medical records: Application for fractures prediction. International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019, 11509 LNAI, 228–239. https://doi.org/10.1007/978-3-030-20915-5_21 Martsenyuk, V., & Semenets, A. (2018). On Code Refactoring for Decision Making Component Combined with the Open-Source Medical Information System. In J. Pejaś, I. El Fray, T. Hyla, & J. Kacprzyk (Eds.), Advances in Soft and Hard Computing. ACS 2018. Advances in Intelligent Systems and Computing (pp. 196–208). https://doi.org/10.1007/978-3-030-03314-9_18