OMDP: An ontology-based model for diagnosis and treatment of diabetes patients in remote healthcare systems

Li Chen, Dongxin Lu, Menghao Zhu, Muhammad Muzammal, Oluwarotimi Williams Samuel, Guixin Huang, Weinan Li and Hongyan Wu

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 5, 1550147719847112

Abstract: Millions of adults have diabetes across the globe and the overall cost for managing diabetic patients has reached up to approximately 250 million. A major constraint in existing ontology-based systems for diagnosing and treating diabetes is the presence of semantic inconsistencies and lack of a comprehensive clinical approach primarily due to consideration of a limited number of classes in the model. In this research, we are focused on building an ontology-based model for diabetic patients by collecting detailed diabetic knowledge of subjects for further diagnosis and treatment. The concept of semantic resources to electronic health record standards is an essential factor for semantic interoperability in remote health monitoring. This study applies semantic web ontology language for developing ontology-based model for diabetic patients to aid doctors in reaching an efficient diagnostic decision about the status of diabetes by applying Semantic Web Rule Language. A total of 766 medical records from clinical environment were selected in this study, and 269 of them were known for developing diabetes. The experimental results suggest that the proposed solution is more accurate in managing diabetes compared to other medical applications. The performance analysis of the ontology-based model for diabetic patients regarding the accuracy of disease prediction, diagnosing diabetes, and recommending medicine is 95%, 98%, and 85%, respectively.

Keywords: Electronic health records; ontology model; diabetes; knowledge-based systems; remote healthcare systems (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:5:p:1550147719847112

DOI: 10.1177/1550147719847112

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