Gyrard Amelie, Gaur Manas, Shekarpour Saeedeh, Thirunarayan Krishnaprasad, Sheth Amit
Knoesis, Wright State University, USA.
University of Dayton, USA.
CEUR Workshop Proc. 2018 Oct;2317.
Our current health applications do not adequately take into account contextual and personalized knowledge about patients. In order to design "Personalized Coach for Healthcare" applications to manage chronic diseases, there is a need to create a Personalized Healthcare Knowledge Graph (PHKG) that takes into consideration a patient's health condition (personalized knowledge) and enriches that with contextualized knowledge from environmental sensors and Web of Data (e.g., symptoms and treatments for diseases). To develop PHKG, aggregating knowledge from various heterogeneous sources such as the Internet of Things (IoT) devices, clinical notes, and Electronic Medical Records (EMRs) is necessary. In this paper, we explain the challenges of collecting, managing, analyzing, and integrating patients' health data from various sources in order to synthesize and deduce meaningful information embodying the vision of the Data, Information, Knowledge, and Wisdom (DIKW) pyramid. Furthermore, we sketch a solution that combines: 1) IoT data analytics, and 2) explicit knowledge and illustrate it using three chronic disease use cases - asthma, obesity, and Parkinson's.
我们当前的健康应用程序没有充分考虑到有关患者的情境化和个性化知识。为了设计用于管理慢性病的“医疗保健个性化教练”应用程序，需要创建一个个性化医疗保健知识图谱（PHKG），该图谱要考虑患者的健康状况（个性化知识），并用来自环境传感器和数据网络（例如疾病的症状和治疗方法）的情境化知识对其进行丰富。为了开发PHKG，有必要汇总来自各种异构源（如物联网（IoT）设备、临床记录和电子病历（EMR））的知识。在本文中，我们解释了从各种来源收集、管理、分析和整合患者健康数据以合成和推导体现数据、信息、知识和智慧（DIKW）金字塔愿景的有意义信息所面临的挑战。此外，我们概述了一个结合了1）物联网数据分析和2）显性知识的解决方案，并使用哮喘、肥胖症和帕金森氏症这三个慢性病用例对其进行说明。
