HealthIoT Ontology for Data Semantic Representation and Interpretation Obtained from Medical Connected Objects

Rhayem, Ahlem; Mhiri, Mohamed; Gargouri, Faïez

doi:10.1109/aiccsa.2017.171

Cited by 22 publications

(15 citation statements)

References 12 publications

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“…And a HealthIoT ontology was proposed to represent the semantics of connected objects and data on the Internet of Medical Things. 43 It enables semantic rule reasoning to assist the clinical decision making.…”

Section: Resultsmentioning

confidence: 99%

A literature review of current technologies on health data integration for patient-centered health management

Peng

Goswami

Bai

2019

Health Informatics J

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Health data integration enables a collaborative utilization of data across different systems. It not only provides a comprehensive view of a patient’s health but can also potentially cope with challenges faced by the current healthcare system. In this literature review, we investigated the existing work on heterogeneous health data integration as well as the methods of utilizing the integrated health data. Our search was narrowed down to 32 articles for analysis. The integration approaches in the reviewed articles were classified into three classifications, and the utilization approaches were classified into five classifications. The topic of health data integration is still under debate and problems are far from being resolved. This review suggests the need for a more efficient way to invoke the various services for aggregating health data, as well as a more effective way to integrate the aggregated health data for supporting collaborative utilization. We have found that the combination of Web Application Programming Interface and Semantic Web technologies has the potential to cope with the challenges based on our analysis of the review result.

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Section: Resultsmentioning

confidence: 99%

A literature review of current technologies on health data integration for patient-centered health management

Peng

Goswami

Bai

2019

Health Informatics J

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“…The quality of the data that WBAN sensors send out is what enables remote patient monitoring. There is a need to understand how data collected from wearable devices can be used to deliver health solutions to users (Rhayem, Mhiri and Gargouri, 2017).…”

Section: Wireless Body Area Networkmentioning

confidence: 99%

Wireless Body Area Network and Sensor Data Interoperability

Alatta¹

2021

Preprint

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The monitoring of maternal and child health, using wearable devices made with wireless sensor technologies, is expected to reduce maternal and child death rates. Wireless sensor technologies have been used in wireless sensor networks to enable the acquisition of data for monitoring machines, smart cities, transportation, asset tracking, and tracking of human activity. Applications based on wireless body area network (WBAN) have been used in healthcare for measuring and monitoring of patient health and activity through integration with wearable devices. Wireless sensors used in WBAN can be cost-effective, enable remote availability, and can be integrated with electronic health record (EHR) management systems. Interoperability of WBAN sensor data with other linked data has the potential to improve health for all, including maternal and child health through the improvement of data access, data quality and healthcare access. This paper presents a survey of the state-of-the-art techniques for managing WBAN sensor data interoperability. The findings in this study will provide reliable support to enable policymakers and health care providers to take action to enhance the use of e-health to improve maternal-child health and reduce the mortality rates of women and children.

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“…For this end, we aim to extend our proposed HealthIoT ontology (Rhayem, Mhiri, & Gargouri, 2017) in order to cover and express diverse contexts that affect the diagnosis and monitoring of both MO's and patients' states missed in the previous version.…”

Section: Semantic Modelling Phasementioning

confidence: 99%

“…This model contains knowledge about the IoT and the healthcare domain and the relationships between them. At this level, we aim to extend our previous work on HealthIoT in Rhayem, Mhiri, and Gargouri (2017) and Rhayem, Mhiri, Salah, and Gargouri (2017), where we proposed a HealthIoT ontology, by implementing new concepts related to the contexts of the medical devices and patients, respectively. Assisting doctors in the exploitation of the collected data by the technique of automated reasoning based on SWT. We consider here the use‐case of gestational diabetes context and we develop the related analysis rules that are based on the generic ontology (HealthIoT). Verifying and configuring the proper functioning of the employed MCO by exploiting its employment context information, in order to guarantee the certainty of the detected data. Developing a clinical decision support system (IoT Medicare system) to implement the proposed knowledge base based on SWT technologies (SPARQL, Jena API) for automatic selection of clinical and configuration services.…”

Section: Introductionmentioning

confidence: 99%

A semantic‐enabled and context‐aware monitoring system for the internet of medical things

et al. 2020

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The emergence of the Internet of Things (IoT) in the medical field has led to the massive deployment of a myriad of medical connected objects (MCOs). These MCOs are being developed and implemented for remote healthcare monitoring purposes including elderly patients with chronic diseases, pregnant women, and patients with disabilities. Accordingly, different associated challenges are emerging and include the heterogeneity of the gathered health data from these MCOs with ever‐changing contexts. These contexts are relative to the continuous change of constraints and requirements of the MCOs deployment (time, location, state). Other contexts are related to the patient (medical record, state, age, sex, etc.) that should be taken into account to ensure a more precise and appropriate treatment of the patient. These challenges are difficult to address due to the absence of a reference model for describing the health data and their sources and linking these data with their contexts. This article addresses this problem and introduces a semantic‐based context‐aware system (IoT Medicare system) for patient monitoring with MCOs. This system is based on a core domain ontology (HealthIoT‐O), that is, designed to describe the semantic of heterogeneous MCOs and their data. Moreover, an efficient interpretation and management of this knowledge in diverse contexts are ensured through SWRL rules such as the verification of the proper functioning of the MCOs and the analysis of the health data for diagnosis and treatment purposes. A case study of gestational diabetes disease management is proposed to evaluate the effectiveness of the implemented IoT Medicare system. An evaluation phase is provided and focuses on the quality of the elaborated semantic model and the performance of the system.

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HealthIoT Ontology for Data Semantic Representation and Interpretation Obtained from Medical Connected Objects

Cited by 22 publications

References 12 publications

A literature review of current technologies on health data integration for patient-centered health management

A literature review of current technologies on health data integration for patient-centered health management

Wireless Body Area Network and Sensor Data Interoperability

A semantic‐enabled and context‐aware monitoring system for the internet of medical things

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