Abstract
Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).
Highlights
The aging and growth of the world’s population has caused traditional health information systems to become more complicated
Server update the data with the new record; (ii) the data wrangling entity prepares the data for the application of data mining tools; and (iii) the OpenEHR record is mapped to fast healthcare interoperability resource (FHIR) resources for the sharing of data through FHIR application programming interfaces (APIs)
Since the Internet of Medical Things (IoMT) devices follow the OneM2M specification, we provide a development framework written in an object-oriented paradigm in C++, which simplifies the development process of the application entity (AE)
Summary
The aging and growth of the world’s population has caused traditional health information systems to become more complicated. A CTL mechanism for the integration of multiple IoMT solutions which consider heterogeneous communication protocols; Management of patient clinical data by medical teams (creation, visualization, update and deletion of electronic records); and Knowledge extraction and analytical studies to support clinical DSS services (for example, by the use of OLAP and-or data mining and-or data warehousing techniques). The main contributions of the platform include: Extension and integration of OpenEHR semantics to the IoMT domain for simplifying the collection and dissemination of data and enhancing IoMT interoperability; CTL programming tools for IoMT solutions and data from isolated devices; Simplified data inspection using OLAP features; and Simplified healthcare knowledge extraction through data mining and machine learning capabilities. As for the structure of the article, it is organized as follows: Section 2 addresses related works and introduces the basics for the platform definition; Section 3 describes the proposed platform and evaluates its performance; Section 4 provides the conclusions and proposes future studies
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