Semantic Modelling of Smart Healthcare Data

Nowadays, healthcare is becoming increasingly connected and increasingly complex. These changes provide opportunities and challenges to the research community. For instance, the enormous volume of data gathered from IoT wearable fitness devices and wellness appliances, if effectively analysed and understood, can be exploited to improve people’s well-being and identify predictive markers of future diseases. However, due to the lack of devices interoperability and heterogeneity of data representation formats, the IoT healthcare landscape is characterised by a pervasive presence of ”data silos” which prevents users and health practitioners from obtaining an overall view of whole knowledge. Semantic web technologies, such as ontologies and inference rules, have been shown as a promising way for the integration and exploitation of data from heterogeneous sources. In this paper, we present a semantic data model useful to: (a) analyse information from unstructured data sources along with generic or domain specific datasets; (b) unify them in an interlinked data processing area. The proposed semantic eHealth system enables automatic inferences and logical reasoning, and can significantly facilitate reuse, exploitation and possible extension of IoT health data sources.