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Kobrinskii B.A.

Federal Research Center 'Computer Science and Control', Russian Academy of Sciences, Moscow, Russia;
N.I. Pirogov Russian National Research Medical University, Ministry of Health of the Russia, Moscow, Russia

Kadykov A.S.

FGBU "Nauchnyĭ tsentr nevrologii" RAMN, Moskva

Poltavskaya M.G.

Sechenov First Moscow State Medical University, Moscow, Russia

Blagosklonov N.A.

Federal Research Center 'Computer Science and Control', Russian Academy of Sciences, Moscow, Russia

Kovelkova M.N.

N.I. Pirogov Russian National Research Medical University, Ministry of Health of the Russia, Moscow, Russia

The principles of the operation of an intellectual system of dynamic risk control and the formation of health recommendations

Authors:

Kobrinskii B.A., Kadykov A.S., Poltavskaya M.G., Blagosklonov N.A., Kovelkova M.N.

More about the authors

Journal: Russian Journal of Preventive Medicine. 2019;22(5): 78‑84

DOI: 10.17116/profmed20192205178

Read: 1703 times

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Table of contents

THE PRINCIPLES OF THE OPERATION OF AN INTELLECTUAL SYSTEM OF DYNAMIC RISK CONTROL AND THE FORMATION OF HEALTH RECOMMENDATIONS
THE PRINCIPLES OF THE OPERATION OF AN INTELLECTUAL SYSTEM OF DYNAMIC RISK CONTROL AND THE FORMATION OF HEALTH RECOMMENDATIONS

To cite this article:

Kobrinskii BA, Kadykov AS, Poltavskaya MG, Blagosklonov NA, Kovelkova MN. The principles of the operation of an intellectual system of dynamic risk control and the formation of health recommendations. Russian Journal of Preventive Medicine. 2019;22(5):78‑84. (In Russ.)
https://doi.org/10.17116/profmed20192205178

 
Подписка 2026 Профилактическая медицина (Russian Journal of Preventive Medicine)
 
МСФ на ЯМ
Использование инфографики авторами научных работ
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Abstract:

Abstract Health maintenance is determined by numerous factors. Their set determines the risk of various manifestations of human disease. This study is devoted to the development of an intelligent system that takes into account personal predictors and possible situations, which determine the level of a risk for diseases (in the model of myocardial infarction and stroke). The semantic network not only combines the attributes of risk factors, but also includes nodes with the recommendations that correspond to the sets of risk factor, which are identified at a particular moment in an individual. This is in line with a personalized approach to disease prevention.

Keywords:

health maintenance
risk factors
intellectual system
knowledge base
myocardial infarction
stroke
personal prevention

Authors:

Kobrinskii B.A.

Federal Research Center 'Computer Science and Control', Russian Academy of Sciences, Moscow, Russia;
N.I. Pirogov Russian National Research Medical University, Ministry of Health of the Russia, Moscow, Russia

Kadykov A.S.

FGBU "Nauchnyĭ tsentr nevrologii" RAMN, Moskva

Poltavskaya M.G.

Sechenov First Moscow State Medical University, Moscow, Russia

Blagosklonov N.A.

Federal Research Center 'Computer Science and Control', Russian Academy of Sciences, Moscow, Russia

Kovelkova M.N.

N.I. Pirogov Russian National Research Medical University, Ministry of Health of the Russia, Moscow, Russia

Close metadata

References:

  1. Zingerman BV. Electronic Health Record — service accessible already by now to all people interested. Vrach i informacionnye tehnologii. 2010;(3):15-25. (In Russ.)
  2. Data-Driven Consumer Health Activation Welltok. Accessed March 24, 2019. https://www.welltok.com/
  3. Yi W-J, Saniie J. Patient Centered Real-Time Mobile Health Monitoring System. Ehealth Telecommun Syst Netw. 2016;05(04):75-94. https://doi.org/10.4236/etsn.2016.54007
  4. Luger GF. Artificial intelligence: structures and strategies for complex problem solving. 46th ed. M: LLC «Williams Publishing». Boston: Addison Wesley, 2003. (In Russ.)
  5. Tyukin IYu, Terekhov VA. Adaptation in nonlinear dynamic systems. M: LKI, 2008. (In Russ.)
  6. Pavlov IP. The Reply of A Physiologist To Psychologists. In Pavlov I.P. Polnoe Sobranie Sochinenii. Moscow, Leningrad: Izdatel’stvo Akademii Nauk SSSR, 1951;3(2):187-188. (In Russ.)
  7. Efremova YuE, Oshchepkova EV, Zhernakova YuV, Chazova IE, Yarovaya EB, Shalnova SA, Rotar OP, Konradi AO, Shlyahto EV, Boytsov SA. Cardiovascular risk factors in people with high normal blood pressure in Russian population (based on data obtained in ESSE-RF epidemiological study). Sistemnye gipertenzii. 2017;14(1):6-11. (In Russ.) https://doi.org/10.26442/2075-082x_14.1.6-11
  8. Dzh.M. Lasta eds. for the International Epidemiological Association. Epidemiological dictionary. 4th ed. M.: Otkryty Institut Zdorov’ya, 2009. (In Russ.)
  9. Boytsov SA, Deev AD, Shalnova SA. Mortality and risk factors for non-communicable diseases in Russia: Specific features, trends, and prognosis. Terapevticheskiy arhiv. 2017;89(1):5-13. (In Russ.) https://doi.org/10.17116/terarkh20178915-13
  10. Kobrinskiy BA. Continuum of transitional states of the body and monitoring the dynamics of children’s health. 2nd ed. M. — Berlin: DirectMEDIA, 2016. (In Russ.)
  11. Arnold S, et al. Electronic Health Records: A Global Perspective: A Work Product of the HIMSS Enterprise Systems Steering Committee and the Global Enterprise Task Force. Healthcare Information and Management Systems Society (HIMSS): Global Enterprise Task Force. 2008.
  12. Zarubina TV, Shvyrev SL, Solovyev VG, Rauzina SE, Rodionov VS, Penzin OV, Surin MYu. Integrated electronic health record: Status and Prospects. Vrach i informacionnye tehnologii. 2016;(2):35-44. (In Russ.)
  13. Samorodskaya IV, Boytsov SA. Subsequent myocardial infarction: risk assessment and prevention. Russian Journal of Cardiology. 2017;(6):139-145. (In Russ.) https://doi.org/10.15829/1560-4071-2017-6-139-145
  14. Price AJ, Wright FL, Green J, Balkwill A, Kan SW, Yang TO, Floud S, Kroll ME, Simpson R, Sudlow CLM, Beral V, Reeves GK. Differences in risk factors for 3 types of stroke: UK prospective study and metaanalyses. Neurology. 2018;90(4):e298-e306. https://doi.org/10.1212/wnl.0000000000004856
  15. Murakami K, Asayama K, Satoh M, Inoue R, Tsubota-Utsugi M, Hosaka M, Matsuda A, Nomura K, Murakami T, Kikuya M, Metoki H, Imai Y, Ohkubo T. Risk Factors for Stroke among Young-Old and Old-Old Community-Dwelling Adults in Japan: The Ohasama Study. J Atheroscler Thromb. 2017;24(3):290-300. https://doi.org/10.5551/jat.35766
  16. Schneider S, Kornejeva A, Vibo R, Kõrv J. Risk Factors and Etiology of Young Ischemic Stroke Patients in Estonia. Stroke Res Treat. 2017;2017:1-7. https://doi.org/10.1155/2017/8075697
  17. Lacey B, Herrington W, Preiss D, Lewington S, Armitage J. The Role of Emerging Risk Factors in Cardiovascular Outcomes. Curr Atheroscler Rep. 2017;19(6). https://doi.org/10.1007/s11883-017-0661-2
  18. Suslina ZA, Varakin YuYa, Vereshhagin NV. Vascular diseases of the brain. Epidemiology, the basics of prevention. M: MEDpress-inform, 2006. (In Russ.)
  19. Kartashov AI, Buzin VV, Glazunov IS, Abol’ian LV. Results of testing the basic questionnaire “SINDI” in the evaluation of prophylaxis program development. Sov Zdravookhr. 1991;(3):45-49. (In Russ.)
  20. Flakh P. Machine Learning. The Art and Science of Algorithms that Make Sense of Data. M: DMK Press, 2015. (In Russ.)
  21. Pang Z, Zheng L, Tian J, Kao-Walter S, Dubrova E, Chen Q. Design of a terminal solution for integration of in-home health care devices and services towards the Internet-of-Things. Enterp Inf Syst. 2013;9(1):86-116. https://doi.org/10.1080/17517575.2013.776118
  22. Boulos M, Yassine A, Shirmohammadi S, Namahoot C, Brückner M. Towards an «Internet of Food»: Food Ontologies for the Internet of Things. Future Internet. 2015;7(4):372-392. https://doi.org/10.3390/fi7040372
  23. Osipov GS. Acquisition of knowledge by intellectual systems: Fundamentals of theory and technology. M: Nauka. Fizmatlit, 1997. (In Russ.)
  24. Grigoriev OG, Molodchenkov AI. Risk Health Evaluation and Selection of Preventive Measures Plan with the Help of Argumental Algorithm. Communications in Computer and Information Science. 2018:280-290. https://doi.org/10.1007/978-3-030-00617-4_26
  25. Osipov GS. Methods of artificial intelligence. 2nd ed. M: FIZMATLIT, 2016. (In Russ.)
  26. Hood L, Galas D. P4 Medicine: Personalized, Predictive, Preventive, Participatory: A Change of View that Changes Everything: A white paper prepared for the Computing Community Consortium committee of the Computing Research Association. December 12, 2008. Accessed March 23, 2019. http://cra.org/ccc/resources/ccc-led-whitepapers/
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