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eHealth Services Based on Monte Carlo Algorithms to Anticipate and Lessen the Progress of Type-2 Diabetes

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Applications of Computational Intelligence (ColCACI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 833))

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Abstract

We present a computer-based eHealth system expected to provide tele-consults aimed to reduce complications due to the diabetes disease in adult population mainly between 30 and 60 years old. The software of the tele-consultations system which is essentially based in probabilities is entirely based in the Monte Carlo technology. This stochastic method is supported with a mathematical model which is build through acquired data that allows us to model and carry out predictions on the glucose’s values in time within a certain statistical error. The idea behind of this eHealth system is the rapid identification of those people with a potential risk to acquire complications derived from the high values of glucose in time. The conclusion derived from this study supports the fact that opportune intervention derived from the tele-consultations might alleviate and to improve the diabetes treatment by employing simple low-cost mobile phones and minimal software applications. We illustrated the prospective implementation of this tele-care system with simulations for people with an old diagnosis of diabetes and demonstrating the prospective role o these eHealth systems aimed to improve the quality of life in the middle and long term. From a combined sample composed by acquired data and Monte Carlo, 3 from 4 diabetes patients might be keeping a desirable control of their glucose’s values with a continuous assistance of an eHealth system.

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Correspondence to Huber Nieto-Chaupis .

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Nieto-Chaupis, H. (2018). eHealth Services Based on Monte Carlo Algorithms to Anticipate and Lessen the Progress of Type-2 Diabetes. In: Orjuela-Cañón, A., Figueroa-García, J., Arias-Londoño, J. (eds) Applications of Computational Intelligence. ColCACI 2018. Communications in Computer and Information Science, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-030-03023-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-03023-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03022-3

  • Online ISBN: 978-3-030-03023-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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