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Sensors 2014, 14(1), 1598-1628; doi:10.3390/s140101598

Ontology-Driven Monitoring of Patient’s Vital Signs Enabling Personalized Medical Detection and Alert

1,* , 2
1 Internet Based Communication Networks and Services, Department of Information Technology—iMinds, Ghent University, Gaston Crommenlaan 8 Box 201, Ghent 9050, Belgium 2 Institute of Computer Science, Foundation for Research and Technology and the Department of Computer Science, University of Crete, Vassilika Vouton, P.O. Box 1385, Heraklion 71110, Greece 3 Department of Informatics Engineering, Technological Educational Institute of Crete, Estavromenos 71004, Greece 4 Computational Medicine Laboratory, FORTH-ICS, N.Plastira 100, Vassilika Vouton, Heraklion 71110, Greece
* Author to whom correspondence should be addressed.
Received: 2 December 2013 / Revised: 10 January 2014 / Accepted: 13 January 2014 / Published: 17 January 2014
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A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs’ monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient’s condition on the most suited device within the physician’s reach.
Keywords: medical workflows; ambient intelligence (AmI); congestive heart failure (CHF); semantic reasoning ; medical alarms; quality of service (QoS) medical workflows; ambient intelligence (AmI); congestive heart failure (CHF); semantic reasoning; medical alarms; quality of service (QoS)
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Hristoskova, A.; Sakkalis, V.; Zacharioudakis, G.; Tsiknakis, M.; De Turck, F. Ontology-Driven Monitoring of Patient’s Vital Signs Enabling Personalized Medical Detection and Alert. Sensors 2014, 14, 1598-1628.

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