Centre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece.
School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Sensors (Basel). 2021 Sep 17;21(18):6230. doi: 10.3390/s21186230.
In this paper, we demonstrate the potential of a knowledge-driven framework to improve the efficiency and effectiveness of care through remote and intelligent assessment. More specifically, we present a rule-based approach to detect health related problems from wearable lifestyle sensor data that add clinical value to take informed decisions on follow-up and intervention. We use OWL 2 ontologies as the underlying knowledge representation formalism for modelling contextual information and high-level concepts and relations among them. The conceptual model of our framework is defined on top of existing modelling standards, such as SOSA and WADM, promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of SHACL constraints and rules to recognise patterns, anomalies and situations of interest based on the predefined and stored rules and conditions. A dashboard visualizes both sensor data and detected events to facilitate clinical supervision and decision making. Preliminary results on the performance and scalability are presented, while a focus group of clinicians involved in an exploratory research study revealed their preferences and perspectives to shape future clinical research using the framework.
在本文中，我们展示了一种知识驱动的框架的潜力，该框架可以通过远程和智能评估来提高护理的效率和效果。更具体地说，我们提出了一种基于规则的方法，从可穿戴生活方式传感器数据中检测与健康相关的问题，为后续和干预决策提供有价值的信息。我们使用 OWL 2 本体作为底层知识表示形式，用于对上下文信息和它们之间的高级概念和关系进行建模。我们框架的概念模型是在现有的建模标准（如 SOSA 和 WADM）之上定义的，促进了可互操作知识图的创建。在符号知识图之上，我们定义了一个基于规则的框架，以 SHACL 约束和规则的形式注入专家知识，以根据预定义的规则和条件识别模式、异常和感兴趣的情况。仪表板可视化传感器数据和检测到的事件，以方便临床监督和决策。本文介绍了性能和可扩展性的初步结果，而参与探索性研究的临床医生焦点小组则表达了他们的偏好和观点，以利用该框架为未来的临床研究提供参考。
