Personal Health Train Architecture with Dynamic Cloud Staging

Scientific advances, especially in the healthcare domain, can be accelerated by making data available for analysis. However, in traditional data analysis systems, data need to be moved to a central processing unit that performs analyses, which may be undesirable, e.g. due to privacy regulations in case these data contain personal information. This paper discusses the Personal Health Train (PHT) approach in which data processing is brought to the (personal health) data rather than the other way around, allowing (private) data accessed to be controlled, and to observe ethical and legal concerns. This paper introduces the PHT architecture and discusses the data staging solution that allows processing to be delegated to components spawned in a private cloud environment in case the (health) organisation hosting the data has limited resources to execute the required processing. This paper shows the feasibility and suitability of the solution with a relatively simple, yet representative, case study of data analysis of Covid-19 infections, which is performed by components that are created on demand and run in the Amazon Web Services platform. This paper also shows that the performance of our solution is acceptable, and that our solution is scalable. This paper demonstrates that the PHT approach enables data analysis with controlled access, preserving privacy and complying with regulations such as GDPR, while the solution is deployed in a private cloud environment

Ontology-Driven IoT System for Monitoring Hypertension

Hypertension is a noncommunicable disease (NCD) that causes global concern, high costs and a high number of deaths. Internet of Things, Ubiquitous Computing, and Cloud Computing enable the development of systems for remote and real-time monitoring of patients affected with NCDs like hypertension. This paper reports on a system for monitoring hypertension patients that was built by employing these techniques. This system allows the vital signs of a patient (blood pressure, heart rate, body temperature) to be captured via sensors built in a wearable device similar to a wristwatch. These signals are transmitted to the patient's mobile device for processing, and the generated clinical data are sent to the cloud to be properly presented and analysed by the health professionals responsible for the patient. To deal with semantic interoperability issues that arise when multiple different devices and system components must interoperate, a semantic model was conceived for this system in terms of ontologies for diseases and devices. This paper also presents the semantic module that we developed and implemented in the cloud to perform reasoning based on this model, demonstrating the potential benefits of incorporating semantic technologies in our system.</p