Decision-oriented mathematical modeling for healthy longevity: A conceptual framework for personalized lifestyle medicine and clinical decision support

Authors

https://doi.org/10.22105/thi.v2i3.50

Abstract

Healthy longevity has emerged as a major goal of modern healthcare, shifting the emphasis from extending lifespan to maximizing healthspan. Although lifestyle interventions effectively prevent chronic diseases and promote healthy ageing, selecting the most appropriate intervention for individual patients remains challenging because of biological heterogeneity, incomplete clinical information, and multiple sources of uncertainty. Existing computational approaches, including artificial intelligence, machine learning, mathematical modeling, and operations research, have shown considerable potential for personalized healthcare; however, they provide limited guidance on selecting the most appropriate method for specific clinical decision-making contexts. This narrative review proposes a decision-oriented conceptual framework for selecting mathematical modeling approaches to support healthy longevity. Evidence from healthy longevity, Precision Lifestyle Medicine, artificial intelligence, mathematical modeling, and operations research was synthesized to classify computational methods according to clinical objectives, dominant sources of uncertainty, and data characteristics. The proposed framework links specific healthcare scenarios with appropriate approaches, including machine learning, Bayesian networks, grey system theory, fuzzy decision models, multi-objective optimization, and digital twins. Unlike previous studies that mainly compare algorithmic performance, the proposed framework emphasizes clinical applicability and decision suitability. It provides practical guidance for selecting computational methods to support personalized lifestyle interventions, improve healthspan, and facilitate the development of intelligent clinical decision-support systems for precision healthcare.

Keywords:

Healthy longevity, Mathematical modeling, Uncertainty, Grey system theory

References

  1. [1] Kennedy, B. K., Berger, S. L., Brunet, A., Campisi, J., Cuervo, A. M., Epel, E. S., ... & Sierra, F. (2014). Geroscience: Linking aging to chronic disease. Cell, 159(4), 709–713. https://doi.org/10.1016/j.cell.2014.10.039
  2. [2] Beard, J. R., Officer, A., De Carvalho, I. A., Sadana, R., Pot, A. M., Michel, J. P., ... & Chatterji, S. (2016). The world report on ageing and health: A policy framework for healthy ageing. The lancet, 387(10033), 2145–2154. https://doi.org/10.1016/S0140-6736(15)00516-4
  3. [3] López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013). The hallmarks of aging. Cell, 153(6), 1194–1217. https://doi.org/10.1016/j.cell.2013.05.039
  4. [4] López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2023). Hallmarks of aging: An expanding universe. Cell, 186(2), 243–278. https://doi.org/10.1016/j.cell.2022.11.001
  5. [5] Masfiah, S., Kurnialandi, A., Meij, J. J., & Maier, A. B. (2025). Definitions of healthspan: A systematic review. Ageing research reviews, 111, 102806. https://doi.org/10.1016/j.arr.2025.102806
  6. [6] Steyerberg, E. W. (2019). Study design for prediction modeling. In Clinical prediction models: a practical approach to development, validation, and updating (pp. 37–58). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-16399-0_3
  7. [7] Ben-Tal, A., Nemirovski, A., & El Ghaoui, L. (2009). Robust optimization. Princeton University Press. https://doi.org/10.1515/9781400831050
  8. [8] Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
  9. [9] Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. https://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193
  10. [10] Julong, D. (1989). Introduction to grey system theory. The journal of grey system, 1(1), 1–24. https://uranos.ch/research/references/Julong_1989/10.1.1.678.3477.pdf
  11. [11] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
  12. [12] Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons. https://www.wiley.com/en-us/Multi-Objective+Optimization+using+Evolutionary+Algorithms-p-9780471873396
  13. [13] Corral-Acero, J., Margara, F., Marciniak, M., Rodero, C., Loncaric, F., Feng, Y., ... & Lamata, P. (2020). The ‘Digital Twin’to enable the vision of precision cardiology. European heart journal, 41(48), 4556–4564. https://doi.org/10.1093/eurheartj/ehaa159

Downloads

Published

2025-09-14

Issue

Vol. 2 No. 3 (2025): Trends in Health Informatics

Section

Articles

How to Cite

Shirin Nejad, J. . ., Salehi, S., & Darvishi, D. (2025). Decision-oriented mathematical modeling for healthy longevity: A conceptual framework for personalized lifestyle medicine and clinical decision support. Trends in Health Informatics, 2(3), 142-149. https://doi.org/10.22105/thi.v2i3.50

Similar Articles

1-10 of 11

You may also start an advanced similarity search for this article.