Methods for identifying medical digital twins and a priori determining the characteristics of patient parameter predictions based on their data

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Abstract

Evaluating the characteristics of predicting patient parameters based on medical data is directly related to the quality of identifying medical digital twins, which predetermined both the direction of scientific research and the structure of the proposed article. Special attention is paid to solving the urgent task of improving the accuracy of the forecast by minimizing absolute and relative errors, as well as increasing the reliability of the estimates obtained by increasing the corresponding probability. This approach opens up great prospects for improving the provision of medical care, including in emergency situations.

About the authors

E. P. Minakov

A.F. Mozhaisky Military Aerospace Academy, Ministry of Defense of Russia

Author for correspondence.
Email: seliverstov-pv@yandex.ru
SPIN-code: 4819-0765

Doctor of Engineering Sciences; Professor

Russian Federation, Saint Petersburg

V. B. Grinevich

S.M. Kirov Military Medical Academy, Ministry of Defense of Russia

Email: seliverstov-pv@yandex.ru
ORCID iD: 0000-0002-1095-8787
SPIN-code: 1178-0242

MD; Professor

Russian Federation, Saint Petersburg

E. V. Kryukov

S.M. Kirov Military Medical Academy, Ministry of Defense of Russia

Email: seliverstov-pv@yandex.ru
ORCID iD: 0000-0002-8396-1936
SPIN-code: 3900-3441

Academician of the Russian Academy of Sciences, MD; Professor

Russian Federation, Saint Petersburg

P. V. Seliverstov

S.M. Kirov Military Medical Academy, Ministry of Defense of Russia

Email: seliverstov-pv@yandex.ru
ORCID iD: 0000-0001-5623-4226
SPIN-code: 6166-7005

Associate Professor, Candidate of Medical Sciences

Russian Federation, Saint Petersburg

References

  1. Sun T., He X., Li Z. Digital twin in healthcare: recent updates and challenges. Digit Health. 2023; 9: 20552076221149652. doi: 10.1177/20552076221149651
  2. Li L., Lei B., Mao C. Digital twin in smart manufacturing. J Ind Inf Integr. 2022; 26: 100289. doi: 10.1016/j.jii.2021.100289
  3. Sun T., He X., Song X. et al. The digital twin in medicine: a key to the future of healthcare? Front Med. 2022; 9: 907066. doi: 10.3389/fmed.2022.907066
  4. Schwartz S.M., Wildenhaus K., Bucher A. et al. Digital twins and the emerging science of self: implications for digital health experience design and “small” data. Front Comput Sci. 2020; 2. doi: 10.3389/fcomp.2020.00031
  5. Николаев В.А., Николаев А.А. Опыт и перспективы использования технологий виртуальной, дополненной и смешанной реальности в условиях цифровой трансформации системы здравоохранения. Медицинские технологии. Оценка и выбор. 2020; 2: 35–42 [Nikolaev V.A., Nikolaev A.A. Virtual, augmented and mixed reality technologies in the context of digitalization of healthcare system. Medical Technologies. Assessment and Choice. 2020; 2: 35–42 (in Russ.)]. doi: 10.17116/medtech20204002135.
  6. Минаков Е.П., Гриневич В.Б., Крюков Е.В. и др. Базы данных потенциальных цифровых двойников программно-моделирующего комплекса прогнозирования ведущих показателей состояния пациентов. Врач. 2025; 36 (10): 28–33 [Minakov E., Grinevich V., Kryukov E. et al. Databases of potential digital doubles of the software-modeling complex for predicting leading indicators of patients' condition. Vrach. 2025; 36 (10): 28–33 (in Russ.)]. doi: 10.29296/25877305-2025-10-05
  7. Saratkar S.Y., Langote M., Kumar P. et al. Digital twin for personalized medicine development. Front Digit Health. 2025; 7: 1583466. doi: 10.3389/fdgth.2025.1583466
  8. Machado T.M., Berssaneti F.T. Literature review of digital twin in healthcare. Heliyon. 2023; 9 (9): e19390. doi: 10.1016/j.heliyon.2023.e19390
  9. Vallée A. Digital twin for healthcare systems. Front Digit Health. 2023; 5: 1253050. doi: 10.3389/fdgth.2023.1253050
  10. Минаков Е.П., Гриневич В.Б., Крюков Е.В. и др. Прогнозирование ведущих показателей состояния пациентов с использованием медицинских цифровых двойников. Врач. 2025; 36 (11): 30–4 [Minakov E., Grinevich V., Kryukov E. et al. Predicting leading indicators of patient status using medical digital twins. Vrach. 2025; 36 (11): 30–4 (in Russ.)]. doi: 10.29296/25877305-2025-11-05
  11. Гриневич В.Б., Крюков Е.В., Минаков Е.П. и др. Концепция применения цифровых двойников для прогнозирования значений ведущих показателей состояния здоровья пациентов. Врач. 2025; 36 (9): 82–6 [Grinevich V., Kryukov E., Minakov E. et al. The concept of using digital twins to predict the values of leading indicators of patients' condition. Vrach. 2025; 36 (9): 83–6 (in Russ.)]. doi: 10.29296/25877305-2025-09-16

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