Abstract
Biomedical Engineering uses computational simulation models that are increasingly refined to represent human physiological systems. These models allow changes and analysis In Silico, optimizing implementation in a real (human) system. This work explores the CVSim computational model of the cardiovascular system, developed and used by MIT and Harvard Medical School, since 1984. The purpose of this work is the prospect of a resilient and adaptive system that by obtaining a mass of data; associated to the hemodynamic behavior of the set: Heart and Ventricular Assist Device (VAD); through simulations, to predict the behavior of this system in an autonomous intelligent environment, which can support the decision making about possible adverse events that may occur. It is intended to consider the profile of the patient with heart disease and the exploration of data: Big Data Analytics.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Cannon, W.B.: Organization for physiological homeostasis. Physiol. Rev. 9(3), 399–431 (1929)
Oomen, P.J.A., Holland, M.A., Bouten, C.V.C., Kuhl, E., Loerakker, S.: Growth and remodeling play opposing roles during postnatal human heart valve development. Sci. Rep. 1–13 (2018)
Selye, H.: The general adaptation syndrome and the diseases of adaptation. J. Clin. Endocrinol. Metab. 6(2), 117–230 (1946)
Cunanan, A.J., et al.: The general adaptation syndrome: a foundation for the concept of periodization. Sport. Med. 1–11 (2018)
Buckner, S.L., Mouser, J.G., Dankel, S.J., Jessee, M.B., Mattocks, K.T., Loenneke, J.P.: The General Adaptation Syndrome: potential misapplications to resistance exercise. J. Sci. Med. Sport 20(11), 1015–1017 (2017)
Nelson, R.J.: An Introduction to Behavioral Endocrinology, 2nd edn. Sinauer Associates-Inc, Sunderland (2000)
Nansel, T.R., Thomas, D.M., Liu, A.: Efficacy of a behavioral intervention for pediatric type 1 diabetes across income. Am. J. Prev. Med. 49(6), 930–934 (2015)
Webb, N.E., Little, B., Loupee-Wilson, S., Power, E.M.: Traumatic brain injury and neuro-endocrine disruption: medical and psychosocial rehabilitation. NeuroRehabilitation 34(4), 625–636 (2014)
Mrosovski, N.: Rheostasis: The Physiology of Change, vol. 19, no. 2. Oxford University Press, New York, Oxford (1990)
Blum, R.W.: Risco e resiliência: sumário para desenvolvimento de um programa, vol. 16, no. 9, August 1977
Masoomi, H., van de Lindt, J.W.: Restoration and functionality assessment of a community subjected to tornado hazard. Struct. Infrastruct. Eng. 14(3), 275–291 (2018)
Bonanno, G.A.: Loss, trauma, and human resilience: have we underestimated the human capacity to thrive after extremely aversive events? Am. Psychol. 59(1), 20–28 (2004)
Chiang, Y.C., Ling, T.Y.: Exploring flood resilience thinking in the retail sector under climate change: a case study of an estuarine region of Taipei City. Sustainability 9(9), 1650 (2017)
Nahayo, L., et al.: Extent of disaster courses delivery for the risk reduction in Rwanda. Int. J. Disaster Risk Reduct. 27, 127–132 (2018)
Timoshenko, S.P.: History of Strength of Materials (Dover Civil and Mechanical Engineering). Dover Publications, New York (1953)
Aliança Brasileira da Indústria Inovadora em Saúde – ABIIS: Saúde 4.0 - Propostas para Impulsionar o Ciclo das Inovações em Dispositivos Médicos (DMAs) no Brasil. Aliança Brasileira da Indústria Inovadora em Saúde – ABIIS, São Paulo (2015)
Silva, E.B., Scoton, M.L.R.P.D., Dias, E.M., Pereira, S.L.: AUTOMAÇÃO & SOCIEDADE - Quarta Revolução Industrial, um olhar para o Brasil. Braspot, Rio de Janeiro (2018)
Stewart, R.: Simulation: the practice of model development and use (2004)
Pidd, M.: Computer Simulation in Management Science, Part I, 4th edn., p. 332. Wiley, New York (1998)
Mattar, S.L.S., Najib, F., Oliveira, B.; Motta, S.: Pesquisa de Marketing: metodologia, planejamento, execução e análise, 7th edn., Rio de Janeiro (2014)
Moza, A., et al.: Parametrization of an in-silico circulatory simulation by clinical datasets – towards prediction of ventricular function following assist device implantation. Biomed. Eng./Biomed. Tech. 62(2), 123–130 (2017)
Centeno, M.A.: An introduction to simulation modeling. In: Winter Simulation Conference, pp. 15–22 (1996)
Dias, J.C., Dias, J.C., Filho, D.J.S.: Aplicação das tecnologias da indústria 4.0 em ambiente inteligente de tomada de decisão médica para pacientes com dav implantado. 14 Congr. da Soc. Lat. Am. Biomateriais, Orgãos Artif. e Eng. Tecidos - SLABO, pp. 74–81 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 IFIP International Federation for Information Processing
About this paper
Cite this paper
Dias, J.C., Dias, J.C., Barboza, M., Sousa Sobrinho, J.R., Santos Filho, D.J. (2018). Systemic Model of Cardiac Simulation with Ventricular Assist Device for Medical Decision Support. In: Camarinha-Matos, L., Adu-Kankam, K., Julashokri, M. (eds) Technological Innovation for Resilient Systems. DoCEIS 2018. IFIP Advances in Information and Communication Technology, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-319-78574-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-78574-5_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78573-8
Online ISBN: 978-3-319-78574-5
eBook Packages: Computer ScienceComputer Science (R0)
