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. 2014 Jan 17;14(1):1598-628.
doi: 10.3390/s140101598.

Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert

Anna Hristoskova  1 , Vangelis Sakkalis  2 , Giorgos Zacharioudakis  3 , Manolis Tsiknakis  4 , Filip De Turck  5
Affiliations

Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert

Anna Hristoskova et al. Sensors (Basel). .

Abstract

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.

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Figures

Figure 1.
Figure 1.
Localization and notification of the responsible physician during an emergency.
Figure 2.
Figure 2.
Ambient intelligence (AmI) patient monitoring and emergency detection framework architecture.
Figure 3.
Figure 3.
Workflow planning and service execution architecture.
Figure 4.
Figure 4.
Definition of the quality of service linked to a specific device.
Figure 5.
Figure 5.
Extension of the standard OWL-Sservice description with the specification of a device on which the service is deployed.
Figure 6.
Figure 6.
Contextual definition of an LCD TV, which is a media device able to display information in a specific patient room on a 1,278 × 771 screen.
Figure 7.
Figure 7.
Ontological definition of a patient's medical state according to a specific model for the heart failure disease. (a) User Context; (b) Patient Record for congestive heart failure.
Figure 8.
Figure 8.
Framingham Heart Study (FHS) added to the HF classification ontology.
See this image and copyright information in PMC

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