A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard
A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard
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DOI：
10.1186/s12911-019-0806-z
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发表时间：
2019-05-10
影响因子：
3.5
通讯作者：
Kwak, Kyung-Sup
中科院分区：
文献类型：
--
作者：
El-Sappagh, Shaker;Ali, Farman;Kwak, Kyung-Sup
背景包括临床决策支持系统（CDSS）在内的移动健康（MH）技术为患者监测和治疗提供了一种有效的方法。移动的CDSS基于实时传感器数据和历史电子健康记录（EHR）数据。原始传感器数据本身没有语义;因此，计算机系统无法自动解释这些数据。此外，传感器数据和EHR医疗数据的互操作性也是一个挑战。从分布式系统收集的EHR数据具有不同的结构、语义和编码机制。因此，构建一个透明的CDSS，可以在任何现有的EHR生态系统中作为便携式即插即用组件工作，需要仔细的设计过程。本体论和医疗标准支持建设的语义智能CDSSs.MethodsThis本文提出了一个全面的MH框架与集成的CDSS能力。这种基于云的系统监测和管理1型糖尿病。任何CDSS的效率主要取决于其知识的质量及其与不同数据源的语义互操作性。为此，本文集中在构建一个语义CDSS的基础上提出FASTO ontology.ResultsThis现实的本体是能够收集，形式化，整合，分析和操纵所有类型的患者数据。它为患者提供完整、个性化和医学直观的护理计划，包括胰岛素方案、饮食、锻炼和教育子计划。这些计划是基于完整的患者档案。此外，所提出的CDSS提供基于从患者的无线体域网收集的生命体征的实时患者监测。这些监测包括实时胰岛素调整，餐时碳水化合物计算和运动建议。FASTO集成了HL 7快速医疗互操作性资源（FHIR），语义传感器网络（SSN）本体，基本形式本体（BFO）2.0和临床实践指南的知名标准。FASTO的当前版本包括9577个类，658个对象属性，164个数据属性，460个个体和140个SWRL规则。FASTO可通过国家生物医学本体中心BioPortal在https://bioportal.bioontology.org/ontologies/FASTO.ConclusionsThe上公开获得，由此产生的CDSS系统可以帮助医生有效和准确地监测更多的患者。此外，农村地区的患者可以依靠该系统来管理他们的糖尿病和紧急情况。
BackgroundMobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the interoperability of sensor data and EHR medical data is a challenge. EHR data collected from distributed systems have different structures, semantics, and coding mechanisms. As a result, building a transparent CDSS that can work as a portable plug-and-play component in any existing EHR ecosystem requires a careful design process. Ontology and medical standards support the construction of semantically intelligent CDSSs.MethodsThis paper proposes a comprehensive MH framework with an integrated CDSS capability. This cloud-based system monitors and manages type 1 diabetes mellitus. The efficiency of any CDSS depends mainly on the quality of its knowledge and its semantic interoperability with different data sources. To this end, this paper concentrates on constructing a semantic CDSS based on proposed FASTO ontology.ResultsThis realistic ontology is able to collect, formalize, integrate, analyze, and manipulate all types of patient data. It provides patients with complete, personalized, and medically intuitive care plans, including insulin regimens, diets, exercises, and education sub-plans. These plans are based on the complete patient profile. In addition, the proposed CDSS provides real-time patient monitoring based on vital signs collected from patients' wireless body area networks. These monitoring include real-time insulin adjustments, mealtime carbohydrate calculations, and exercise recommendations. FASTO integrates the well-known standards of HL7 fast healthcare interoperability resources (FHIR), semantic sensor network (SSN) ontology, basic formal ontology (BFO) 2.0, and clinical practice guidelines. The current version of FASTO includes 9577 classes, 658 object properties, 164 data properties, 460 individuals, and 140 SWRL rules. FASTO is publicly available through the National Center for Biomedical Ontology BioPortal at https://bioportal.bioontology.org/ontologies/FASTO.ConclusionsThe resulting CDSS system can help physicians to monitor more patients efficiently and accurately. In addition, patients in rural areas can depend on the system to manage their diabetes and emergencies.