OMDP: An ontology-based model for diagnosis and treatment of diabetes patients in remote healthcare systems
OMDP: An ontology-based model for diagnosis and treatment of diabetes patients in remote healthcare systems
复制标题
OMDP：一种基于本体的模型，用于远程医疗系统中糖尿病患者的诊断和治疗
DOI：
10.1177/1550147719847112
复制
发表时间：
2019-05-01
影响因子：
2.3
通讯作者：
Wu, Hongyan
中科院分区：
文献类型：
--
作者：
Chen, Li;Lu, Dongxin;Wu, Hongyan
全球有数百万成年人患有糖尿病，管理糖尿病患者的总成本已高达约 2.5 亿美元。现有基于本体的糖尿病诊断和治疗系统的一个主要限制是存在语义不一致和缺乏全面的临床方法，这主要是由于模型中考虑的类别数量有限。在这项研究中，我们致力于通过收集受试者详细的糖尿病知识，为进一步的诊断和治疗建立一个基于本体的糖尿病患者模型。电子健康记录标准的语义资源概念是远程健康监测中语义互操作性的重要因素。本研究应用语义网络本体语言为糖尿病患者开发基于本体的模型，以帮助医生通过应用语义网络规则语言对糖尿病状态做出有效的诊断决策。本研究共选取了766份来自临床环境的病历，其中269份已知患有糖尿病。实验结果表明，与其他医疗应用相比，所提出的解决方案在管理糖尿病方面更准确。基于本体的糖尿病患者模型在疾病预测、诊断糖尿病和推荐药物方面的准确性分别为 95%、98% 和 85%。
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.