The concept of the agent-based model for predicting a patient’s general health in the process of aging

Agent-based modeling is actively used for modeling human health. The main advantages of an agent-based approach in this field are the capability to implement a modular approach to health and to account for individual patient indicators. The article presents the concept of a flexible and expandable agent model of the patient, which performs a long-term prediction of the patient's condition based on short-term test treatments administered to them, including geroprophylactic, and by predicting the patient's reaction to exposure in order to prevent future possible diseases with regard to both calendar and biological age. All interactions of the model agents are reduced to assessing the effectiveness of the anti-aging measures in the form of a calculated bio-age which characterizes the degree of decrease in the functional capacity of the organism. As part of the concept, the central agents “Patient”, “Aging Process” and “Impact” are highlighted in the model as well as a number of lower-level agents associated with the agent “Patient”. Lower-level agents are responsible for modeling the physiological processes of body systems or diseases, for example, a chronic disease is allocated its own agent, which affects the patient's condition during the modeling. The types of model agents are extensible, which makes it possible to develop this concept of the model. The paper presents the testing of the agent model concept to identify the effectiveness of the impact on the patient following on from the assessment of changes in the biological age before and after geroprophylactic therapy.

1. Klinke D.J. 2nd. Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study. J. Immunother Cancer. 2015;3:27. DOI: 10.1186/s40425-015-0069-x.

2. McDaniel M., Carter J., Keller J., White S., Baird A. Open Source PKPD Frame Work: Tutorial on the BioGears Engine [published online ahead of print, 2018 Nov 8]. CPT Pharmacometrics Syst Pharmacol. 2018;8(1):12–25. DOI: 10.1002/psp4.12371.

3. Hester R.L., Brown A.J., Husband L., et al. HumMod: A Modeling Environment for the Simulation of Integrative Human Physiology. Front Physiol. 2011;2:12. DOI: 10.3389/fphys.2011.00012.

4. Proshin A.P., Solodyannikov Y.V. Mathematical modeling of blood circulation system and its practical application. Autom Remote Control. 2006;67:329–341. Available by: https://doi.org/10.1134/S000511790602010X.

5. Kutumova E., Kiselev I., Sharipov R., Lifshits G., Kolpakov F. Thoroughly Calibrated Modular Agent-Based Model of the Human Cardiovascular and Renal Systems for Blood Pressure Regulation in Health and Disease. Front Physiol. 2021;12:746300. DOI:10.3389/fphys.2021.746300.

6. Day T.E., Ravi N., Xian H., Brugh A. An Agent-Based Modeling Template for a Cohort of Veterans with Diabetic Retinopathy. PLoS One. 2013 Jun 21;8(6):e66812. DOI: 10.1371/journal.pone.0066812. PMID: 23805280; PMCID: PMC3689690.

7. Veloso M. An agent-based simulation model for informed shared decision making in multiple sclerosis. Mult Scler Relat Disord. 2013;2(4):377–84. DOI: 10.1016/j.msard.2013.04.001. PMID: 25877849.

8. Hum R.S., Kleinberg S. Replicability, Reproducibility, and Agent-based Simulation of Interventions. AMIA Annu Symp Proc. 2017:959–968. PMID: 29854163; PMCID: PMC5977631.

9. Bronson Weston, Benjamin Fogal, Daniel Cook, Prasad Dhurjati, An agent-based modeling framework for evaluating hypotheses on risks for developing autism: Effects of the gut microbial environment. Medical Hypotheses. 2015;84(4):395–401. Available by: https://doi.org/10.1016/j.mehy.2015.01.027.

10. Auchincloss A.H., Diez Roux A.V. A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health. Am J Epidemiol. 2008;168(1):1–8. DOI: 10.1093/aje/kwn118. PMID: 18480064.

11. Broomhead T., Ballas D., Baker S.R. Neighbourhoods and oral health: Agent-based modelling of tooth decay. Health Place. 2021;71:102657. DOI: 10.1016/j.healthplace.2021.102657. PMID: 34543838.

12. Li Y., Kong N., Lawley M.A., Pagán J.A. Using systems science for population health management in primary care. J Prim Care Community Health. 2014;5(4):242–6. DOI: 10.1177/2150131914536400. PMID: 24879655.

13. Marshall B.D., Galea S. Formalizing the role of agent-based modeling in causal inference and epidemiology. Am J Epidemiol. 2015;181(2):92–9. DOI: 10.1093/aje/kwu274. PMID: 25480821; PMCID: PMC4351348.

14. Johnson S.D., Groff E.R. Strengthening theoretical testing in criminology using agent-based modeling. J. Res. Delinquen. 2014;51(4):509–525. Available by: https://doi.org/10.1177/0022427814531490.

15. Cerda M., Tracy M., Ahern J., Galea S. Addressing population health and health inequalities: the role of fundamental causes. Am. J. Publ. Health. 2014;104(4):609–619. Доступно по: https://doi.org/10.2105/AJPH.2014.302055.

16. Paolillo R., Jager W. Simulating acculturation dynamics between migrants and locals in relation to network formation. Soc. Sci. Comput. Rev. 2019;1–22. Доступно по: https://doi.org/10.1177/0894439318821678.

17. Wu J.W., Yaqub A., Ma Y. et al. Biological age in healthy elderly predicts aging-related diseases including dementia. Sci Rep. 2021;11:1–10. DOI 10.1038/s41598-021-95425-5.

18. Putin E., Mamoshina P., Aliper A., Korzinkin M., Moskalev A., Kolosov A., Ostrovskiy A., Cantor C., Vijg J., Zhavoronkov A. Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging. 2016;8(5):1021–1030. DOI: 10.18632/aging.100968.

19. Samorodskaya Irina & Starinskaya M. Biological age and the rate of aging as a risk factor for non-communicable diseases and deaths. Profilakticheskaya meditsina. 2016;19:41. DOI: 10.17116/profmed201619541-46.

20. Kirkland J.L. Translating the Science of Aging into Therapeutic Interventions. Cold Spring Harb Perspect Med. 2016;6(3):a025908. DOI:10.1101/cshperspect.a025908.

21. Myakotnykh V.S. Theory and practice of modern gerontology: monograph / Meshchaninov V.N., Borovkova T.A., Sidenkova A.P. Yekaterinburg: LLC «IIC «Quality Mark»;2022. 280 p.: ill., table. – ISBN 978-5-89895-990-6. – Text: direct. (In Russ.).

22. Myakotnykh V.S., Ostapchuk E.S., Meshchaninov V.N. et al. Pathological aging: the main «targets», age-associated diseases, gender characteristics, geroprophylaxis: textbook. Moscow: New Format; 2021. 128 p. ISBN 978-5-91556-922-4. (In Russ.).

23. NET documentation. Available by: https://docs.microsoft.com/en-us/dotnet (accessed on 30.10.2022).

24. Advanced Message Queuing Protocol. Available at: https://www.amqp.org/ (accessed on 30.10.2022).

25. Stonebraker M., Rowe L.A., Hirohama M. The Implementation Of Postgres. IEEE Transactions on Knowledge and Data Engineering. 1990;2(1):340–355. DOI:10.1109/69.50912.

26. Adya A., Blakeley J.A., Melnik S., Muralidhar S. Anatomy of the ADO.NET entity framework. Proceedings of the 2007 ACM SIGMOD international conference on Management of data. 2007;1:877–888. DOI: 10.1145/1247480.1247580.

27. Open Neural Network Exchange. Available at: https://onnx.ai (accessed on 06.04.2022).

28. Limanovskaya O.V., Gavrilov I.V., Meshchaninov V.N., Shcherbakov D.L., Kolos E.N. Modeling the biological age of patients based on their functional indicators. Modeling, Optimization and Information Technology. 2021:9(2):1-16. DOI: 10.26102/2310-6018/2021.33.2.028 (In Russ).

29. Tanatkanova A. K., Zhambaeva A. K. Building of client-server applications. Nauka i obrazovanie segodnya. 2019;6-2(41). (In Russ.).