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

Abstract

A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease that may be of great significance. The dedicated clinical personnel should be contacted immediately and possibly intervene in time before an acute state is reached, by changing medication, or any other interventions, in order to ensure patient safety. This paper presents an Ambient Intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure. The remote monitoring environment, enhanced with semantic technologies, provides a personalized, accurate and fully automated emergency alerting system that smoothly interacts with the personal physician, regardless his/her physical location in order to ensure in time intervention in case of an emergency. The proposed framework is able to change context at runtime in case new medical services are registered, new rules are defined, or in case of network overload and failure situations. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.