Approach to Software Integration of Heterogeneous Sources of Medical Data Based on Microservice Architecture

Cover Page

Cite item

Full Text

Abstract

The task of processing medical information is currently being solved in our country and abroad by means of heterogeneous medical information systems, mainly at the local and regional levels. The ever-increasing volume and complexity of the accumulated information, along with the need to ensure transparency and continuity in the processing of medical data (in particular, for bronchopulmonary diseases) in various organizations, requires the development of a new approach to integrating their heterogeneous sources. At the same time, an important requirement for solving the problem is the possibility of web-oriented implementation, which will make the corresponding applications available to a wide range of users without high requirements for their hardware and software capabilities. The paper considers an approach to the integration of heterogeneous sources of medical information, which is based on the principles of building microservice web architectures. Each data processing module can be used independently of other program modules, providing a universal entry point and the resulting data set in accordance with the accepted data schema. Sequential execution of processing steps implies the transfer of control to the corresponding program modules in the background according to the Cron principle. The schema declares two types of data schemas - local (from medical information systems) and global (for a single storage system), between which the corresponding display parameters are provided according to the principle of constructing XSLT tables. An important distinguishing feature of the proposed approach is the modernization of the medical information storage system, which consists in creating mirror copies of the main server with periodic replication of the relevant information. At the same time, the interaction between clients and data storage servers is carried out according to the type of content delivery systems with the creation of a connection session between end points based on the principle of the nearest distance between them, calculated using the haversine formula. The computational experiments carried out on test data on bronchopulmonary diseases showed the effectiveness of the proposed approach both for loading data and for obtaining them by individual users and software systems. Overall, the reactivity score of the corresponding web-based applications was improved by 40% on a stable connection.

About the authors

N. I Yusupova

Ufa State Aviation Technical University

Email: yussupova@ugatu.ac.ru
Karl Marx St. 12

G. R Vorobeva

Ufa State Aviation Technical University

Email: gulnara.vorobeva@gmail.com
Karl Marx St. 12

R. Kh Zulkarneev

Bashkir State Medical University of the Ministry of Health of Russia

Email: zurustem@mail.ru
Karl Marx St. 9/1

References

  1. Snyder M., Zhou W. Big data and health // The Lancet. Digital Health. 2019. Vol. 1, iss. 6. P. E252-E-254
  2. Комолов А.В. Обзор медицинских стандартов передачи электронной информации // Аллея науки. 2019. Т. 2. № 2(29). С. 909-913
  3. Martínez-Costa C., Schulz S. HL7 FHIR: Ontological Reinterpretation of Medication Resources // Studies in Health Technology and Informatics. 2017. No. 235. P. 451– 455. doi: 10.3233/978-1-61499-753-5-451.
  4. Mukhiya S., Rabbi F., Pun V. [et al.]. A GraphQL approach to Healthcare Information Exchange with HL7 FHIR // Procedia Computer Science. 2019. No. 160. P.338-345. doi: 10.1016/j.procs.2019.11.082.
  5. Hong N., Wang K., Wu S. [et al.] An Interactive Visualization Tool for HL7 FHIR Specification Browsing and Profiling // Journal of Healthcare Informatics Research. 2019. No. 3. doi: 10.1007/s41666-018-0043-8.
  6. Елоев М.С. Опыт внедрения медицинской информационной системы в многопрофильном амбулаторно-поликлиническом учреждении // Военно-медицинский журнал. 2014. Т. 335. № 9. С. 4-13
  7. Alqudah A., Al-Emran M., Shaalan K. Medical data integration using HL7 standards for patient’s early identification // PLOS ONE. 2021. No. 16. P. e0262067. doi: 10.1371/journal.pone.0262067.
  8. Brogan J., del Pilar M., López A. [et al.] Scalable data systems require creating a culture of continuous learning // EBioMedicine Home (Part of Lancet Discovery Science). 2021. Vol. 74, P. 103738, doi: https://doi.org/10.1016/j.ebiom.2021.103738
  9. Prakash C., Amit Sh. National Institute of Malaria Research-Malaria Dashboard (NIMR-MDB): A digital platform for analysis and visualization of epidemiological data // The Lancet Regional Health. 2022. P. 100030.
  10. Balicer R., Arnon A. Digital health nation: Israel's global big data innovation hub // The Lancet. 2017. Vol. 389, iss. 10088, p. 2451-2453. doi: https://doi.org/10.1016/S0140-6736(17)30876-0
  11. Grabner M, Molife C, Wang L, Winfree K, Cui Z, Cuyun Carter G, Hess L. Data Integration to Improve Real-world Health Outcomes Research for Non–Small Cell Lung Cancer in the United States: Descriptive and Qualitative Exploration // JMIR Cancer 2021;7(2):e23161. doi: 10.2196/23161
  12. Mate S, Köpcke F, Toddenroth D, Martin M, Prokosch H-U, Bürkle T,et al. Ontology-Based Data Integration between Clinical and Research Systems // PLoS ONE. 2015. No. 10(1). P. e0116656. PMID: 25588043.
  13. Lin YL, Trbovich P, Kolodzey L, Nickel C, Guerguerian A. Association of Data Integration Technologies With Intensive Care Clinician Performance: A Systematic Review and Meta-analysis // JAMA Netw Open. 2019. No. 2(5). P. e194392. doi: 10.1001/jamanetworkopen.2019.4392.
  14. Scheurwegs E., Luyckx K. [et al.]. Data integration of structured and unstructured sources for assigning clinical codes to patient stays // Journal of the American Medical Informatics Association. 2016. Vol. 23, Iss. e1. P. e11–e19, https://doi.org/10.1093/jamia/ocv115
  15. Martínez-García M., Hernández-Lemus E. Data Integration Challenges for Machine Learning in Precision Medicine // Front. Med. 2022. No. 8:784455. doi: 10.3389/fmed.2021.784455.
  16. Di Stefano A., La Corte A., Scatá M. Health Mining: a new data fusion and integration paradigm // Proceedings of CIBB. 2014. Vol. 1. P. 98-107.
  17. Kamdar M.R., Fernández J.D., Polleres A. [et al.] Enabling Web-scale data integration in biomedicine through Linked Open Data // Digit. Med. 2019. No. 2. P. 90. https://doi.org/10.1038/s41746-019-0162-5
  18. Dhayne H., Haque R., Kilany R., Taher Y. In Search of Big Medical Data Integration Solutions. A Comprehensive Survey // IEEE Access. 2019. PP. 1-10. doi: 10.1109/ACCESS.2019.2927491.
  19. Kük E., Erel-Ozcevik M. Access protocol aware controller design for eMBB traffic in SD-CDN // Computer Networks. 2022. No. 205. P. 08686. doi: 10.1016/j.comnet.2021.108686.
  20. Zerwas J., Poese I., Schmid S., Blenk A. On the Benefits of Joint Optimization of Reconfigurable CDN-ISP Infrastructure // IEEE Transactions on Network and Service Management. 2021. PP. 105-112. doi: 10.1109/TNSM.2021.3119134.
  21. Vorobev, A.; Soloviev, A.; Pilipenko, V.; Vorobeva, G.; Sakharov, Y. An Approach to Diagnostics of Geomagnetically Induced Currents Based on Ground Magnetometers Data // Appl. Sci. 2022, 12, 1522. https://doi.org/10.3390/app12031522
  22. Choi, B. Python Network Automation Labs: cron and SNMPv3. In: Introduction to Python Network Automation. Apress, Berkeley, CA, 2021.doi: 10.1007/978-1-4842-6806-3_15.
  23. Vorobev, A.V., Pilipenko, V.A., Enikeev, T.A., Vorobeva, G.R. Geoinformation system for analyzing the dynamics of extreme geomagnetic disturbances from observations of ground stations // Computer Optics. 2020. No. 44(5). P. 782–790.
  24. Barlas K., Stefaneas P. An Algebraic Specification / Schema for JSON // Journal of Engineering Research and Sciences. 2022. No. 1. doi: 10.55708/js0105025.
  25. Rajendran L., Veilumuthu R. An Efficient Distributed Model for XMLised Blob Data Generation // International Journal of Computer Applications. 2011. No. 22. doi: 10.5120/2561-3519.
  26. Yang Z., Jiang K., Lou M. [et al.] Defining health data elements under the HL7 development framework for metadata management // Journal of Biomedical Semantics. 2022. No. 13. doi: 10.1186/s13326-022-00265-5.
  27. Rahmatulloh A., Gunawan R., Nursuwars F. Performance comparison of signed algorithms on JSON Web Token // IOP Conference Series: Materials Science and Engineering. 2019. No. 550. P. 012023. doi: 10.1088/1757-899X/550/1/012023.
  28. Beltran V. Characterization of web single sign-on protocols // IEEE Communications Magazine. 2016. No. 54. P. 24-30. doi: 10.1109/MCOM.2016.7514160.
  29. Jones M., Bradley J., Sakimura N., JSON Web Token (JWT)., RFC 7519, doi: 10.17487/RFC7519, May 2015, https://www.rfc-editor.org/info/rfc7519.
  30. Cai Sh., Chen K., Liu M. [et al.] Garbage collection and data recovery for N2DB // Tsinghua Science and Technology. 2022. No. 27. P. 630-641. doi: 10.26599/TST.2021.9010016.
  31. Garcia A., May D., Nutting E. Integrated Hardware Garbage Collection // ACM Transactions on Embedded Computing Systems. 2021. No. 20. P. 1-25. doi: 10.1145/3450147.
  32. Zhang Q., Bernstein P., Berger D., Chandramouli B. Redy: remote dynamic memory cache // Proceedings of the VLDB Endowment. 2021. No. 15. P. 766-779. doi: 10.14778/3503585.3503587.
  33. Tserpes K., Pateraki M., Varlamis I. Strand: scalable trilateration with Node.js // Journal of Cloud Computing. 2019. No. 8. doi: 10.1186/s13677-019-0142-y.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).