Abstract
The aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls.
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Acknowledgments
The current study was partially supported by “the 2007–2013 NOP for Research and Competitiveness for the Convergence Regions (Calabria, Campania, Puglia and Sicilia)” with code PON04a3_00139 - Project Smart Health and Artificial intelligence for Risk Estimation.
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This article is part of the Topical Collection on Mobile Systems.
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Melillo, P., Orrico, A., Scala, P. et al. Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients. J Med Syst 39, 109 (2015). https://doi.org/10.1007/s10916-015-0294-3
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DOI: https://doi.org/10.1007/s10916-015-0294-3