Covid-19: Early identification and monitoring of the 2019 novel Coronavirus (Covid-19) disease community spread

Covid-19：2019新型冠状病毒（Covid-19）疾病社区传播的早期识别和监测

• 批准号: 552041-2020
• 负责人: Ikki, Salama
• 金额: $ 3.64万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=702928
• 关键词: Covid; 19; Early; identification; monitoring

The lack of health care information technology to leverage Electronic Health Records information (EHR) into useful insights locks the benefits of EHR systems. For example, COVID-19 is an unexpected outbreak that causes infections of the nose, throat and lungs and may cause death. In Canada, there are approximately 55,000 COVID-19. COVID-19 presents a sophisticated challenge and burden to the Canadian health care system because of its unexpected nature and high probability of potential life-loss. Currently, there is a knowledge gap in the development of early patient identification strategies of COVID-19 disease on a larger scale without in-person examination of patients at hospitals, due to the lack of technology to enable the remote identification and monitoring of this disease. The objectives of this study are: 1) The development of innovative predictive analytics algorithms to analyze patient EHR with nasopharynx swabs data to find undiagnosed patients at risk of spreading COVID-19. This objective will be achieved by correctly extracting and integrating COVID-19 semantic information from the multiple heterogeneous datasets that exist in the Canadian EHR systems, develop new predictive algorithms utilizing the extracted information, develop a new algorithm to estimate the probability of a patient to spread the disease, and develop a dynamic clustering algorithm to cluster patients into homogenous groups of different COVID-19 spreading probability (e.g., Low, High), 2)The development of an innovative platform to integrate predictive analytics algorithms with different medical databases into a shared decision-making mechanism. The shared decision-making mechanism can be used by physicians and policymakers to share information and work toward decisions about how to control the spread of the disease.

由于缺乏医疗保健信息技术来将电子健康记录信息（EHR）转化为有用的见解，因此限制了EHR系统的优势。例如，COVID-19是一种意想不到的爆发，会导致鼻子、喉咙和肺部感染，并可能导致死亡。在加拿大，约有55，000名COVID-19患者。COVID-19对加拿大医疗保健系统提出了复杂的挑战和负担，因为它具有意想不到的性质和潜在生命损失的高概率。目前，由于缺乏远程识别和监测这种疾病的技术，在大规模开发COVID-19疾病的早期患者识别策略方面存在知识差距，而无需在医院对患者进行亲自检查。本研究的目的是：1）开发创新的预测分析算法，以分析具有鼻咽拭子数据的患者EHR，以发现有传播COVID-19风险的未确诊患者。这一目标将通过以下方式实现：从加拿大EHR系统中存在的多个异构数据集中正确提取和整合COVID-19语义信息，利用提取的信息开发新的预测算法，开发新的算法来估计患者传播疾病的概率，并开发动态聚类算法以将患者聚类到不同COVID-19传播概率的同质组（例如，低，高），2）开发一个创新平台，将预测分析算法与不同的医疗数据库集成到共享决策机制中。医生和政策制定者可以使用共享决策机制来共享信息并努力做出有关如何控制疾病传播的决策。