DIGITAL HEALTH SOLUTIONS FOR COVID-19: COVID-19 ONGOING MONITORING (COMMUNITY)

COVID-19 数字健康解决方案：COVID-19 持续监测（社区）

• 批准号: 10329864
• 负责人: LUCA FOSCHINI
• 金额: $ 56万
• 依托单位: EVIDATION HEALTH, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-14 至 2021-09-13
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10329864
• 关键词: Algorithms; Android; COVID-19; COVID-19 detection; Cellular Phone; Communities; Data; Detection; Goals; Individual; Internet; Machine Learning; Modeling; Monitor; Participant; Patient Self-Report; Persons; Population; Secure; Surveys; United States National Institutes of Health; base; data hub; digital health; experience; health data; influenza surveillance; wearable sensor technology

The goal of this proposal is to develop a COVID-19 detection algorithm based on self-report survey data and wearable sensor data. Data from 25K COVID-19 Experiences participants and 25K Large-scale Flu Surveillance (COVID-19 Questions added March 2020) will be used with an existing machine learning model to develop this new detection algorithm, which will be validated in a large-scale pilot population to identify individuals with undiagnosed COVID-19. Evidation will incorporate the model into an established web and multi-platform (Android, iOS) smartphone platform called Achieve which allows users to share person-generated health data (PGD) from their everyday lives. Data collected under this project will be deidentified and securely transmitted to an NIH data hub.

该提案的目标是开发一种基于自我报告调查数据和可穿戴传感器数据的COVID-19检测算法。来自25，000名COVID-19体验参与者和25，000名大规模流感监测（2020年3月添加的COVID-19问题）的数据将与现有的机器学习模型一起使用，以开发这种新的检测算法，该算法将在大规模试点人群中进行验证，以识别未确诊的COVID-19患者。Evidation将把该模型整合到一个名为Achieve的网络和多平台（Android，iOS）智能手机平台中，该平台允许用户从日常生活中分享个人生成的健康数据（PGD）。在该项目下收集的数据将被去识别并安全地传输到NIH数据中心。