Model identification for homeostatic data**

稳态数据的模型识别**

• 批准号: 537690-2018
• 负责人: vanVeen, Lennaert
• 金额: $ 1.74万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=667553
• 关键词: Model; identification; homeostatic; data

Various common diseases, such as diabetes and epilepsy, can be managed but not cured. Managing the disease often requires invasive or expensive measurements. The data thus produced can be unreliable and infrequent. A new alternative has presented itself in recent years due to the miniaturization of sensors and their integration with everyday electronics like watches and phones. Homeostatic biomarkers like heart rate, blood pressure and core temperature can be measured continuously and stored in a central repository. The challenge is, to infer from such secondary data early warning signals for the onset of pathological states like hypoglycaemia and seizures. **In this project, we will develop practical algorithms to model and detect anomalies in homeostatic data. These algorithms will combine data-centric techniques like de-noising and curve fitting with elements of modelling of the underlying physiological processes. Ultimately, the purpose of these algorithms is to predict, in real time, the future time course of the relevant biomarkers for individual patients. **The research will take place jointly at the R&D laboratory of Klick Health, the world's largest independent health marketing and commercialization agency, and in the Modelling and Computational Science graduate program at UOIT, a young and STEM-focused university.**The planned research will strengthen the Research as a Service portfolio offered by Klick Health to a variety of clients in the pharmaceutical industry and support Klick Health's goal to enhance their credentials as a top-tier research company. **The student working on the project will be offered a unique opportunity to experience industrial R&D at one of Toronto's fastest growing tech companies, and is expected to continue working towards his or her graduate degree after completing the joint project.******

各种常见疾病，如糖尿病和癫痫，可以控制，但不能治愈。管理这种疾病通常需要侵入性或昂贵的测量。由此产生的数据可能不可靠且不频繁。近年来，由于传感器的小型化及其与手表和手机等日常电子产品的集成，出现了一种新的替代方案。稳态生物标志物，如心率，血压和核心温度可以连续测量并存储在中央存储库中。挑战在于，从这些次级数据中推断出低血糖和癫痫发作等病理状态发作的早期预警信号。** 在这个项目中，我们将开发实用的算法来建模和检测稳态数据中的异常。这些算法将联合收割机以数据为中心的技术，如去噪和曲线拟合与基本生理过程的建模元素相结合。最终，这些算法的目的是真实的预测个体患者的相关生物标志物的未来时间进程。** 这项研究将在世界上最大的独立健康营销和商业化机构Klick Health的研发实验室以及年轻的STEM大学UOIT的建模和计算科学研究生课程中联合进行。计划中的研究将加强Klick Health向制药行业的各种客户提供的研究即服务组合，并支持Klick Health提高其作为顶级研究公司的资质的目标。** 参与该项目的学生将获得一个独特的机会，在多伦多发展最快的科技公司之一体验工业研发，并有望在完成联合项目后继续攻读研究生学位。