New measures to decrease the burden from cardiovascular morbidity are of great socio-economic importance. The aim of the study was to create artificial intelligence technology incorporating various methods and approaches for presenting and using knowledge to assess and predict individual risks of developing cardiovascular events. The following risk presenting models were used: scoring system, multivariate Weibull and logistic regression, artificial neural networks; an ontological approach for explicit representation of knowledge and the construction of software solvers generating an explanation in easy-to-interpret terms. One of the main technological solutions used was the IACPaaS cloud platform, which has infrastructure and intelligent service development technology. The result of the study is a hybrid technology for risk assessment and forecasting, presented in the article by the architecture of the decision support services produced, the ontology of knowledge, the knowledge base for cardiology and the methods for implementing services. The key feature of the technology is its scalability by connecting new microservices implemented on arbitrary heterogeneous architectures. The scope of application ranges from cardiology research of risk assessment and prognosis to medical practitioners.
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2. Shalnova SA, Oganov RG, Deev AD, et al. Comorbidities of ischemic heart disease with other non-communicable diseases in adult population: age and risk factors association. Cardiovascular Therapy and Prevention. 2015; 14(4): 44-51. (In Russ.) doi: 10.15829/1728-8800-2015-4-44-51.
3. Nashef S.AM, François Roques, Linda D Sharples, et al. EuroSCORE II European Journal of Cardio-Thoracic Surgery. 2012; 41(4): 734-745. doi: 10.1093/ejcts/ezs043.
4. Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary. A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019; 74(10): 1376-414. doi: 10.1016/j.jacc.2019.03.009.
5. SCORE2 working group and ESC cardiovascular risk collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. European Heart Journal. 2021; 42(25): 2439-2454. doi: 10.1093/eurheartj/ehab309.
6. Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. New England Journal of Medicine. 1979; 300(24): 1350-1358. doi: 10.1056/NEJM197906143002402.
7. Gusev AV, Gavrilov DV, Novitsky RE, et al. Improvement of cardiovascular risk assessment using machine learning methods. Russian Journal of Cardiology. 2021; 26(12): 4618. (In Russ.) doi: 10.15829/1560-4071-2021-4618.
8. Komar` P, Dmitriev V, Ledyaeva A, et al. Prognoznaya analitika v sisteme zdravooxraneniya. Analiticheskij otchet. EverCare. 2021. (In Russ.) Available at: https://evercare.ru/news/prognoznaya-analitika-v-sisteme-zdravookhraneniya.
9. Gusev AV, Gavrilov DV, Korsakov IN, et al. Prospects for the use of machine learning methods for predicting cardiovascular diseases. Vrach i informacionnye tehnologii. 2019; 3: 41-47. (In Russ.)
10. Zhang L, Niu M, Zhang H, et al. Nonlaboratory-based risk assessment model for coronary heart disease screening: Model development and validation. Int J Med Inform. 2022; 162: 104746. doi: 10.1016/j.ijmedinf.2022.104746.
11. Wang T, Qiu RG, Yu M, Zhang R. Directed disease networks to facilitate multiple-disease risk assessment modeling. Decision Support Systems. 2020; 129: 113171. doi: 10.1016/j.dss.2019.113171.
12. Ambale-Venkatesh B, Yang X, Wu CO, et al. Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis. Circulation research. 2017; 121(9): 1092-1101. doi. 10.1161/CIRCRESAHA.117.311312.
13. Benjamins JW, Hendriks T, Knuuti J, et al. A primer in artificial intelligence in cardiovascular medicine. Neth Heart J. 2019; 27(9): 392-402. doi: 10.1007/s12471-019-1286-6.
14. Duan H, Sun Z, Dong W, Huang, Z. Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome. BMC Med Inform Decis Mak. 2019; 19(5): 1-11. doi: 10.1186/s12911-018-0730-7.
15. Kagiyama N, Shrestha S, Farjo PD, Sengupta PP. Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease. Journal of the American Heart Association. 2019; 8(17): e012788. doi: 10.1161/JAHA.119.012788.
16. Krittanawong C, Zhang H, Wang Z, et al. Artificial Intelligence in Precision Cardiovascular Medicine. Journal of the American College of Cardiology. 2017; 69(21): 2657-2664. doi: 10.1016/j.jacc.2017.03.571.
17. Myers PD, Scirica BM, Stultz CM. Machine Learning Improves Risk Stratification After Acute Coronary Syndrome. Scientific Reports. 2017; 7(1): 1-12. doi: 10.1038/s41598-017-12951-x.
18. Pieszko K, Hiczkiewicz J, Budzianowski P, et al. Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers. Desiase Markers. 2019; 2019. ID 9056402: 1-9. doi: 10.1155/2019/9056402.
19. Shah SH, Arnett D, Houser SR, et al. Opportunities for the Cardiovascular Community in the Precision Medicine Initiative. Circulation. 2016. 133(2): 226–231. doi: 10.1161/CIRCULATIONAHA.115.019475.
20. Gribova VV, Petryaeva MV, Shalfeeva.A. Cloud decision support service in cardiology based on formalized knowledge. The Siberian Journal of Clinical and Experimental Medicine. 2020; 35(4): 32-38. (In Russ.) doi: 10.29001/2073-8552-2020-35-4-32-38.
21. Gribova V, Fedorischev L, Moskalenko Ph, Timchenko V. Interaction of cloud services with external software and its implementation on the IACPaaS platform. CEUR Workshop Proceedings. 2021; 2930: 8-18.
22. Nevzorova VA, Brodskaya TA, Shakhgeldyan KI, et al. Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai. Cardiovascular Therapy and Prevention. 2022; 21(1): 2908. (In Russ.) doi: 10.15829/1728-8800-2022-2908.
23. Geltser BI, Shakhgeldyan KI, Rublev VY, et al. Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting. Kardiologiia. 2020; 60(10): 38-46. (In Russ.) doi: 10.18087/cardio.2020.10.n1170.
24. Gribova VV, Petryaeva MV, Okun DB, Shalfeeva EA. Medical diagnosis ontology for intelligent decision support systems. Ontology of designing. 2018; 8(1): 58-73. (In Russ.) doi: 10.18287/2223-9537-2018-8-1-58-73.
25. Petryaeva MV, Shalfeeva EA. Cardiovascular risk knowledge base for assessment and forecast of state. Informatika i sistemy upravleniya. 2021; 3(69): 112-125. (In Russ.) doi: 10.22250/isu.2021.69.112-125.
26. Gribova VV, Moskalenko PhM, Shahgeldyan CI, et al. Concept for a Heterogeneous Biomedical Information Warehouse. Information technologies. 2019; 25(2): 97-66. (In Russ.) doi: 10.17587/it.25.97-106.
For citation
Gribova V.V., Geltser B.I., Shakhgeldyan K.I., Petryaeva M.V., Shalfeeva E.A., Kosterin V.V. Hybrid technology of risk assessment and prognosis in cardiology. Medical doctor and information technology. 2022; 3: 24-35. doi: 10.25881/18110193_2022_3_24.
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