DIGITAL HEALTH SOLUTIONS FOR COVID-19: PERSONALIZED ANALYTICS WEARABLE BIOSENSOR PLATFORM FOR EARLY DETECTION OF COVID-19 DECOMPENSATION

该项目的目标是开发一个基于人工智能的数据分析和云计算平台，与美国食品和药物管理局批准的可穿戴设备配合使用，以创建一个个性化的基线指数，该指数可以指示新冠肺炎检测呈阳性的患者健康状况的变化。该项目涉及新冠肺炎失代偿指数的开发和验证，该指数建立在PhysIQ现有的基于可穿戴生物传感器的分析平台之上。数据将从400名人类受试者中收集，这些受试者既是入院前的受试者(被发现对新冠肺炎呈阳性)，也是已经住院和接受COVID治疗然后出院的受试者。这一组合人群将包括新冠肺炎失代偿案例(事件案例)和新冠肺炎未导致任何类型失代偿的案例(非事件案例)。400名患者的数据集将被划分为训练子集和测试子集。将使用接收器操作员特征(ROC)曲线下面积(AUC)作为性能衡量标准来评估性能。根据该项目收集的数据将被识别并安全地传输到国家卫生研究院的数据中心。

The goal of this project is to develop an artificial intelligence-based data analytics and cloud computing platform, paired with U.S. Food and Drug Administration (FDA)-cleared wearable devices, to create a personalized baseline index that could indicate a change in health status for patients who have tested COVID-19 positive. The project involves the development and validation of a COVID-19 Decompensation Index (CDI) that builds off physIQ’s existing wearable biosensor-derived analytics platform. Data will be collected from 400 human subjects that are both pre-hospitalization subjects (found to be positive for COVID-19) and subjects that have been hospitalized and treated for COVID and then discharged. This combined population will consist of COVID-19 decompensation cases (event cases) and cases for which COVID-19 did not result in any kind of decompensation (non-event cases). The 400-patient dataset will be partitioned into a training subset and a testing subset. Performance will be assessed using receiver operator characteristics (ROC) area under the curve (AUC) as the metric of performance. Data collected under this project will be deidentified and securely transmitted to an NIH data hub.