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A. Henaien et al.
studies that has highlighted the importance of the development of a powerful healthcare system. The study conclude that an integrated multidisciplinary
infrastructure allowing interoperability and scalability is crucial. From that stage
to nowadays, innovations in Information and Communication Technologies have
radically changed healthcare services, created several manners for collecting and
managing data effectively and provided several solutions to e-healthcare challenges [11]. The health sector is nowadays in its knowledge age: data, information, and knowledge are used in real time to support effective integration of
prevention, treatment, and recovery services across healthcare services. Computers are not only used to provide health services but also to improve health itself
through the management of the knowledge base and the automatic support of
decisions. Therefore, healthcare applications are now exchanging and performing
not only an enormous volume of data but also an important quantity of information and a large knowledge base Fig. 2. Thus, the semantic interoperability is
becoming a crucial feature that is hard to imagine a healthcare or clinical system
architecture without it [15]. The IoT ontologies appear as a suitable alternative
to exchange knowledge per providing the required semantics to augment the
data contained in the information model, and that to support service management operations [32]. Ontological models are becoming commonly used models
in healthcare systems providing a flexible approach to integrate data and share
meaning and able to assist inferring meaning [24,33]. Often ontology-based systems are using rule-based decision support system in order to assure an active
and assisted monitors of patients [33]. However, a majority of those systems are
not performing an automatic updates of the knowledge base. Hence, we propose
in this paper a semantic-based healthcare monitoring system with seamless integration of many intricate existing knowledge, ontologies and ML technologies.
It is a dynamic rule-based system, which infers information and medical recommendations based on the interaction of IoT input captured data, subjective
and objective knowledge and a dynamic rule base updates by a ML algorithm
based rules generator. The main contribution of this work is a combinations of
semantic rules reasoning and ML reasoning to provide a new ubiquitous context
awareness situation framework for healthcare monitoring systems. Those two
highly modern and very powerful tools: semantic rules based reasoning and ML
based reasoning, should provide complementary and supportive roles in the collection and processing of data, identification of clinical situations and automated
decision making for supporting medical activities.
Paper Organization: The structure of this paper is as follows: Sect. 2 briefly
introduces the related works and background. Section 3 outlines our methodology. Section 4 describes in details our proposed system. Section 5 presents context
and situation awareness ontological modelling. Section 6 focuses on the knowledge and reasoning component engine. Section 7 evaluates the proposed system.
Concluding remarks and perspectives are presented in Sect. 8.
A. Henaien et al.
studies that has highlighted the importance of the development of a powerful healthcare system. The study conclude that an integrated multidisciplinary
infrastructure allowing interoperability and scalability is crucial. From that stage
to nowadays, innovations in Information and Communication Technologies have
radically changed healthcare services, created several manners for collecting and
managing data effectively and provided several solutions to e-healthcare challenges [11]. The health sector is nowadays in its knowledge age: data, information, and knowledge are used in real time to support effective integration of
prevention, treatment, and recovery services across healthcare services. Computers are not only used to provide health services but also to improve health itself
through the management of the knowledge base and the automatic support of
decisions. Therefore, healthcare applications are now exchanging and performing
not only an enormous volume of data but also an important quantity of information and a large knowledge base Fig. 2. Thus, the semantic interoperability is
becoming a crucial feature that is hard to imagine a healthcare or clinical system
architecture without it [15]. The IoT ontologies appear as a suitable alternative
to exchange knowledge per providing the required semantics to augment the
data contained in the information model, and that to support service management operations [32]. Ontological models are becoming commonly used models
in healthcare systems providing a flexible approach to integrate data and share
meaning and able to assist inferring meaning [24,33]. Often ontology-based systems are using rule-based decision support system in order to assure an active
and assisted monitors of patients [33]. However, a majority of those systems are
not performing an automatic updates of the knowledge base. Hence, we propose
in this paper a semantic-based healthcare monitoring system with seamless integration of many intricate existing knowledge, ontologies and ML technologies.
It is a dynamic rule-based system, which infers information and medical recommendations based on the interaction of IoT input captured data, subjective
and objective knowledge and a dynamic rule base updates by a ML algorithm
based rules generator. The main contribution of this work is a combinations of
semantic rules reasoning and ML reasoning to provide a new ubiquitous context
awareness situation framework for healthcare monitoring systems. Those two
highly modern and very powerful tools: semantic rules based reasoning and ML
based reasoning, should provide complementary and supportive roles in the collection and processing of data, identification of clinical situations and automated
decision making for supporting medical activities.
Paper Organization: The structure of this paper is as follows: Sect. 2 briefly
introduces the related works and background. Section 3 outlines our methodology. Section 4 describes in details our proposed system. Section 5 presents context
and situation awareness ontological modelling. Section 6 focuses on the knowledge and reasoning component engine. Section 7 evaluates the proposed system.
Concluding remarks and perspectives are presented in Sect. 8.