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.

中文翻译：

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概念验证在多发性硬化症护理中的应用

在本文中，我们展示了知识驱动框架通过远程和智能评估提高护理效率和有效性的潜力。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，这些数据增加了临床价值，以便对后续和干预做出明智的决定。我们使用 OWL 2 本体作为基础知识表示形式，用于对上下文信息和高级概念以及它们之间的关系进行建模。我们框架的概念模型是在现有建模标准（如 SOSA 和 WADM）之上定义的，促进了可互操作的知识图谱的创建。在符号知识图谱之上，我们定义了一个基于规则的框架，用于以 SHACL 约束和规则的形式注入专家知识，以根据预定义和存储的规则和条件识别感兴趣的模式、异常和情况。仪表板可视化传感器数据和检测到的事件，以促进临床监督和决策。介绍了性能和可扩展性的初步结果，同时参与探索性研究的临床医生焦点小组揭示了他们使用该框架塑造未来临床研究的偏好和观点。