Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients.
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.
Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Via Pansini, 5, Naples, 80138, Italy, paolo.melillo@unina2.it.
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D000058:Accidental Falls; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D002318:Cardiovascular Diseases / Q000503:physiopathology; D000067917:Cloud Computing; D016494:Computer Security; D004562:Electrocardiography; D005260:Female; D006801:Humans; D006973:Hypertension / Q000503:physiopathology*; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D009068:Movement; D011446:Prospective Studies; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013686:Telemetry / Q000295:instrumentation*; D014584:User-Computer Interface