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

Author

Listed:
  • Li Chen
  • Dongxin Lu
  • Menghao Zhu
  • Muhammad Muzammal
  • Oluwarotimi Williams Samuel
  • Guixin Huang
  • Weinan Li
  • Hongyan Wu

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.

Suggested Citation

  • Li Chen & Dongxin Lu & Menghao Zhu & Muhammad Muzammal & Oluwarotimi Williams Samuel & Guixin Huang & Weinan Li & Hongyan Wu, 2019. "OMDP: An ontology-based model for diagnosis and treatment of diabetes patients in remote healthcare systems," International Journal of Distributed Sensor Networks, , vol. 15(5), pages 15501477198, May.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:5:p:1550147719847112
    DOI: 10.1177/1550147719847112
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    References listed on IDEAS

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    1. Dhavalkumar Thakker & Stan Karanasios & Emmanuel Blanchard & Lydia Lau & Vania Dimitrova, 2017. "Ontology for cultural variations in interpersonal communication: Building on theoretical models and crowdsourced knowledge," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(6), pages 1411-1428, June.
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    Cited by:

    1. Maryam Al-Thawadi & Farag Sallabi & Mamoun Awad & Khaled Shuaib & Muhammad Raza Naqvi & Hadda Ben Elhadj, 2022. "A-SHIP: Ontology-Based Adaptive Sustainable Healthcare Insurance Policy," Sustainability, MDPI, vol. 14(3), pages 1-20, February.

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