Inventory reference ISSN 1812-7231 Klin.inform.telemed. Volume 10, Issue 11, 2014, Pages 45–53
Author(s) G. Knyshov1, A. Kovalenko2, E. Nastenko3, S. Siromakha1, А. Demin4, S. Svistunov5, A. Pezentsali2, A. Yakovenko3, O. Romanuk2
1Amosov National Institute of Cardiovascular Surgery NAMS of Ukraine, Kyiv
2International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences (NAS) of Ukraine and Ministry of Education and Science (MES) of Ukraine, Kyiv
3National Technical University of Ukraine "Kyiv Polytechnic University"
4Institute for Scintillation Materials National Academy of Sciences of Ukraine, Kharkiv
5Bogolyubov Institute for Theoretical Physics of the National Academy of Sciences of Ukraine, Kyiv
Article title Establishment and implementation of Grid-system in medical diagnostic cardiac department
Introduction. Currently, there is an intense development of information technology in medicine that can store and process large amounts of digital information. One such technology is the Grid system.
Purpose. For the collection and processing of medical digital images of large amounts for their long-term storage, and data mining it is necessary to create a system that should correspond to the modern international standards.
Results and discussion. The article presents the steps for creating a Grid-system for the collection and processing of medical digital images.
The main task in creation and implementation of Grid-technologies are: the primary digital images, their long-term storage and data mining. Data Mining is needed to identify patients at high risk of complications of heart failure after surgery. This will allow to adjust the treatment process as a whole, and to motivate doctors to accumulation of information and analysis of data.
Conclusion. The development of Grid-technologies in medicine is important and promising for obtaining new scientific knowledge and to facilitate daily practice of medical personnel.
Keywords Grid-system; Digital images; Data mining; Database; Information system
1. The Grid, Blueprint for a New computing Infrastructure. Ed. by I. Foster, C. Kesselman, Morgan Kaufmann Publ., 1998, 593 p.
2. The Grid 2: Blueprint for a New Computing Infrastructure. Ed. by I. Foster, C. Kesselman, Morgan Kaufmann Publ., 2004, 748 p.
3. Dyuck V., Samoylenko А. Data Mining: uchebniy kurs [Data Mining: training course]. SPb., Piter Publ., 2001, 368 c. (In Russ.).
4. Grigoryev S. G., Yunkerov V. I., Klimenko N. B. Logisticheskaya regresiya. Mnogomernye metody statisticheskogo analiza kategorialnyh dannih medicinskih issledovaniy. Uch. posobie. [Logistic regression. Multivariate statistical analysis of categorical data for medical research. Textbook]. SPb, 2001, pp. 10–21. (In Russ.).
5. Kim Dzh.-O., Myuller C.W., Klekka W.R. et al; Factor, discriminant and cluster analysis. Ed. J. S. Enyukova. Moscow, Finance and Statistics Publ., 215 p. (In Russ.).
6. Jakulin A., Bratko I. Quantifying and Visualizing Attribute Interactions. An Approach Based on Entropy, PKDD, 2004, vol. 3, pp. 229–240.
7. Yakovenko А. V., Rudenko А. V., Nastenko Е. А., Rudenko N. L., Pavlov V. А. Identifying the structure of risk factors of acute heart failure in the early postoperative period. Vostochnoevropeyskiy jurnal peredovih tehnologiy [East European J. of advanced technologies], 2013, vol. 3, iss. 10 (63), pp. 4–8. (In Russ.).
Full-text version http://kit-journal.com.ua/en/viewer_en.html?doc/2014_11/8.pdf