Knowledge-based system as a context-aware approach for the Internet of medical connected objects

Recently, there has been growing and widespread interest in context-aware applications related to e-healthcare services. These services, especially during emergency periods, such as global pandemic situations, can significantly facilitate the diagnosis of the health status by taking into account the patient's context and available medical knowledge. E-health tools are complex systems that base their functioning on rapidly mutable information, such as medical guidelines, and must be able to guarantee reliability, transparency in the decision process, trustworthiness, and privacy preservation. Moreover, to make them effective, the dynamic nature of knowledge and its context are two other key factors that should be taken into consideration during the design process as well. Modern medical connected objects (MCOs) provide a valuable means to easily reach a significant percentage of the population, while Knowledge Graph (KG) technologies provide a feasible and effective way to enable them to deal with the inherent complexity that characterizes e-healthcare systems. This study aims to devise new models and architectures which exploit the synergy between the KG and Internet of Things technology to tackle the challenges in designing knowledge-based systems as a context-aware approach for the Internet of MCOs. Here, KG standards facilitate the combination and alignment of different models, making it no longer necessary to subscribe to just one healthcare model and fully describing the different components related to the e-healthcare services.