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Information-Logical Model of the Scientific and Technological Potential of Preventive and Personalized Medicine

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Abstract

The personalization of medicine is a modern concept in health care. The paper outlines the goals and objectives of preventive and personalized medicine. A multilevel hierarchical information-logical structure has been formed for the system modeling of development trends in preventive and personalized medicine. Methods for the multiaspect evaluation and selection of promising high technologies in preventive and personalized medicine are proposed.

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REFERENCES

  1. Larichev, O.I., Verbal’nyi analiz reshenii (Verbal Decision Analysis), Moscow: Nauka, 2006.

  2. Matorin, S.I. and Mikhelev, V.V., System-object determinant analysis: Constructing a domain taxonomy, Sci. Tech. Inf. Process., 2022, vol. 49, no. 5, pp. 325–332. https://doi.org/10.3103/S0147688222050069

    Article  Google Scholar 

  3. Matorin, S.I. and Mikhelev, V.V., System-object determinant analysis: Construction of genetic and partitive classification of the subject area, Sci. Tech. Inf. Process., 2023, vol. 50, no. 6.

  4. Matorin, S.I. and Mikhelev, V.V., System-object determinant analysis: Partitive classification using the formal-semantic normative system, Sci. Tech. Inf. Process., 2023, vol. 50, no. 6.

  5. Matorin, S.I., Petrovsky, A.B., Pronichkin, S.V., Sternin, M.Yu., and Shepelev, G.I., Approaches to defining scientific priorities in healthcare and medicine: The foreign experience, Tr. Inst. Sist. Anal., 2019, vol. 69, no. 3, pp. 68–79. https://doi.org/10.14357/20790279190306

    Article  Google Scholar 

  6. Matorin, S.I., Petrovsky, A.B., Pronichkin, S.V., Sternin, M.Yu., and Shepelev, G.I., Approaches to defining scientific priorities in healthcare and medicine: The experience of the Soviet Union, Sci. Tech. Inf. Process., 2021, vol. 48, no. 6, pp. 476–482. https://doi.org/10.3103/S0147688221060083

    Article  Google Scholar 

  7. Matorin, S.I., Petrovsky, A.B., Pronichkin, S.V., Sternin, M.Yu., and Shepelev, G.I., Approaches to defining scientific priorities in healthcare and medicine: The Russian experience, Sci. Tech. Inf. Process., 2020, vol. 48, no. 6, pp. 483–492. https://doi.org/10.3103/s0147688221060095

    Article  Google Scholar 

  8. Pal’tsev, M.A., Belushkina, N.N., and Chaban, E.A., 4P-medicine as a new model of healthcare in the RUSSIAN FEDERATION, ORGZDRAV: Nov., Mneniya, Obuchenie. Vestn. Vysshei Shk. Organizatsii Zdravookhraneniya, 2015, no. 2, pp. 48–54.

  9. Petrovskii, A.B., Gruppovoi verbal’nyi analiz reshenii (Group Verbal Decision Analysis), Moscow: Nauka, 2019.

  10. Petrovsky, A.B. and Lobanov, V.N., Multi-criteria choice in the attribute space of large dimension: Multi-method technology PAKS-M, Sci. Tech. Inf. Process., 2015, vol. 42, no. 6, pp. 470–480. https://doi.org/10.3103/S0147688215060106

    Article  Google Scholar 

  11. Petrovsky, A.B., Pronichkin, S.V., Sternin, M.Yu., and Shepelev, G.I., The information and logical model of national scientific and technological potential, Sci. Tech. Inf. Process., 2018, vol. 46, no. 6, pp. 422–433. https://doi.org/10.3103/s014768821906008x

    Article  Google Scholar 

  12. Decree of the Government of the Russian Federation dated December 28, 2012 no. 2580-r On Approval of the Strategy of Development of Medical Science in the Russian Federation to 2025. http://government.ru/docs/all/85877/.

  13. Burau, V., Nissen, N., Terkildsen, M., and Væggemose, U., Personalised medicine and the state: A political discourse analysis, Health Policy, 2021, vol. 125, no. 1, pp. 122–129. https://doi.org/10.1016/j.healthpol.2020.10.005

    Article  PubMed  Google Scholar 

  14. Conley, V., Daack-Hirsch, S., Halbmaier, K., and Shaw, L., Bringing personalized medicine to a PACT program: A quality improvement project, J. Am. Psychiatric Nurses Assoc., 2020, vol. 26, no. 1, pp. 77–85. https://doi.org/10.1177/1078390319826687

    Article  Google Scholar 

  15. Cirillo, D. and Valencia, A., Big data analytics for personalized medicine, Curr. Opin. Biotechnol., 2019, vol. 58, pp. 161–167. https://doi.org/10.1016/j.copbio.2019.03.004

    Article  CAS  PubMed  Google Scholar 

  16. Davis, J., Kumbale, C., Zhang, Q., and Voit, E., Dynamical systems approaches to personalized medicine, Curr. Opin. Biotechnol., 2019, vol. 58, pp. 168–174. https://doi.org/10.1016/j.copbio.2019.03.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Djulbegovic, B. and Guyatt, G., Progress in evidence-based medicine: A quarter century on, Lancet, 2017, vol. 390, no. 10092, pp. 415–423. https://doi.org/10.1016/s0140-6736(16)31592-6

    Article  PubMed  Google Scholar 

  18. Figueres, C., Landrigan, P., and Fuller, R., Tackling air pollution, climate change, and NCDs: Time to pull together, Lancet, 2018, vol. 392, no. 10157, pp. 1502–1503. https://doi.org/10.1016/s0140-6736(18)32740-5

    Article  PubMed  Google Scholar 

  19. Hellman, D., Trial and error: Can good science be bad medicine?, New Republic, 1998, vol. 218, pp. 16–19.

    Google Scholar 

  20. Hemingway, H., Asselbergs, F., Danesh, J., Dobson, R., Maniadakis, N., Maggioni, A., Van Thiel, G., Cronin, M., Brobert, G., Vardas, P., Anker, S., Grobbee, D., and Denaxas, S., Big data from electronic health records for early and late translational cardiovascular research: Challenges and potential, Eur. Heart J., 2018, vol. 39, no. 16, pp. 1481–1495. https://doi.org/10.1093/eurheartj/ehx487

    Article  PubMed  Google Scholar 

  21. Horsley, S., Morling, J., Khaw, F., and Day, M., Evaluating an “incident control” approach to non-communicable disease, Public Health, 2021, vol. 197, pp. 1–5. https://doi.org/10.1016/j.puhe.2021.04.031

    Article  CAS  PubMed  Google Scholar 

  22. Li, Yu., Xu, H., Cai, D., Zhu, S., Liu, X., Zhao, Ye., Zhang, Z., Bian, Ya., Xue, M., and Zhang, L., Integration of transcriptomic, proteomic and metabolomic data to reveal the biological mechanisms of AAI injury in renal epithelial cells, Toxicol. Vitro, 2020, vol. 70, p. 105054. https://doi.org/10.1016/j.tiv.2020.105054

    Article  CAS  Google Scholar 

  23. Martinez, R., Lloyd-Sherlock, P., Soliz, P., Ebrahim, S., Vega, E., Ordunez, P., and McKee, M., Trends in premature avertable mortality from non-communicable diseases for 195 countries and territories, 1990–2017: A population-based study, Lancet Global Health, 2020, vol. 8, no. 4, pp. e511–e523. https://doi.org/10.1016/s2214-109x(20)30035-8

    Article  PubMed  Google Scholar 

  24. Morrato, E., Implementation science: Ensuring the return on our research investment, Neurorehabilitation Neural Repair, 2018, vol. 32, no. 9, pp. 762–764. https://doi.org/10.1177/1545968318794904

    Article  PubMed  Google Scholar 

  25. Naithani, N., Sinha, S., Misra, P., Vasudevan, B., and Sahu, R., Precision medicine: Concept and tools, Med. J. Armed Forces India, 2021, vol. 77, no. 3, pp. 249–257. https://doi.org/10.1016/j.mjafi.2021.06.021

    Article  PubMed  PubMed Central  Google Scholar 

  26. Pauli, C., Hopkins, B., Prandi, D., Shaw, R., Fedrizzi, T., Sboner, A., Sailer, V., Augello, M., Puca, L., Rosati, R., Mcnary, T., Churakova, Ye., Cheung, C., Triscott, J., Pisapia, D., Rao, R., Mosquera, J., Robinson, B., Faltas, B., Emerling, B., Gadi, V., Bernard, B., Elemento, O., Beltran, H., Demichelis, F., Kemp, C., Grandori, C., Cantley, L., and Rubin, M., Personalized in vitro and in vivo cancer models to guide precision medicine, Cancer Discovery, 2017, vol. 7, no. 5, pp. 462–477. https://doi.org/10.1158/2159-8290.cd-16-1154

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ho, D., Quake, S.R., McCabe, E.R.B., Chng, W.J., Chow, E.K., Ding, X., Gelb, B.D., Ginsburg, G.S., Hassenstab, J., Ho, Ch.-M., Mobley, W.C., Nolan, G.P., Rosen, S.T., Tan, P., Yen, Yu., and Zarrinpar, A., Enabling technologies for personalized and precision medicine, Trends Biotechnol., 2020, vol. 38, no. 5, pp. 497–518. https://doi.org/10.1016/j.tibtech.2019.12.021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sallis, J., Big issues for preventive medicine, Preventive Med., 2012, vol. 55, no. 6, pp. 531–532. https://doi.org/10.1016/j.ypmed.2012.07.004

    Article  Google Scholar 

  29. Di Sanzo, M., Cipolloni, L., Borro, M., La Russa, R., Santurro, A., Scopetti, M., Simmaco, M., and Frati, P., Clinical applications of personalized medicine: A new paradigm and challenge, Curr. Pharm. Biotechnol., 2017, vol. 18, no. 3, pp. 194–203.

    Article  CAS  PubMed  Google Scholar 

  30. Schork, N., Artificial intelligence and personalized medicine, Precision Medicine in Cancer Therapy, Von Hoff, D. and Han, D., Eds., Cancer Treatment and Research, vol. 178, Cham: Springer, 2019, pp. 265–283. https://doi.org/10.1007/978-3-030-16391-4_11

  31. Ten, J. and Hassim, M., Improving the sensitivity of safety and health index assessment in optimal molecular design framework, Comput. Chem. Eng., 2018, vol. 124, pp. 238–252. https://doi.org/10.1016/j.compchemeng.2018.12.012

    Article  CAS  Google Scholar 

  32. Thompson, B., Privileging the preventive medicine physician: A solution in search of a problem?, Preventive Med., 2019, vol. 118, pp. 352–353. https://doi.org/10.1016/j.ypmed.2018.11.018

    Article  Google Scholar 

  33. Todaka, E., Shiga, S., Poore, K., Fenichel, P., and Mori, C., A pilot education program in collaboration with World Health Organization to increase knowledge and awareness amongst medical students of “Environmental Preventive Medicine,” Toxicol. Lett., 2018, vol. 295, p. S256. https://doi.org/10.1016/j.toxlet.2018.06.1042

    Article  Google Scholar 

  34. Toebes, B., Hesselman, M., Mierau, J., and Van Dijk, J., A renewed call for transdisciplinary action on NCDs, BMC Int. Health Hum. Rights, 2020, vol. 20, no. 1, pp. 241–254. https://doi.org/10.1186/s12914-020-00241-z

    Article  Google Scholar 

  35. Tretter, F. and Löffler-Stastka, H., Medical knowledge integration and “systems medicine”: Needs, ambitions, limitations and options, Med. Hypotheses, 2019, vol. 133, p. 109386. https://doi.org/10.1016/j.mehy.2019.109386

    Article  PubMed  Google Scholar 

  36. Voit, E., Networks and dynamic models in systems medicine: Overview, Systems Medicine, Elsevier, 2020, vol. 3, pp. 1–7. https://doi.org/10.1016/b978-0-12-801238-3.11661-7

    Article  Google Scholar 

  37. Waaijer, M., Westendorp, R., Goldeck, D., Gunn, D., Pawelec, G., Stijntjes, M., Slagboom, P., and Maier, A., Assessment of health status by molecular measures in adults ranging from middle-aged to old: Ready for clinical use?, Exp. Gerontol., 2017, vol. 87, pp. 175–181. https://doi.org/10.1016/j.exger.2016.03.020

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was supported by the Russian Foundation for Basic Research (project 19-29-047mk).

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Authors and Affiliations

  1. Federal Research Center “Computer Science and Control” RAS, Moscow, Russia

    A. B. Petrovsky, S. V. Pronichkin & G. I. Shepelev

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  1. A. B. Petrovsky
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  2. S. V. Pronichkin
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  3. G. I. Shepelev
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Correspondence to A. B. Petrovsky.

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Petrovsky, A.B., Pronichkin, S.V. & Shepelev, G.I. Information-Logical Model of the Scientific and Technological Potential of Preventive and Personalized Medicine. Sci. Tech. Inf. Proc. 50, 618–628 (2023). https://doi.org/10.3103/S0147688223060138

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