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（FDA）批准的可穿戴设备配对，以创建一个个性化的基线指数，该指数可以指示COVID-19检测呈阳性的患者的健康状况变化。 该项目涉及COVID-19失代偿指数（CDI）的开发和验证，该指数基于physIQ现有的可穿戴生物传感器衍生分析平台。 将从400名人类受试者中收集数据，这些受试者均为住院前受试者（发现COVID-19呈阳性）和已住院并接受COVID治疗然后出院的受试者。 该合并人群将包括COVID-19失代偿病例（事件病例）和COVID-19未导致任何类型失代偿的病例（非事件病例）。 将400例患者数据集划分为训练子集和测试子集。 将使用受试者工作特征（ROC）曲线下面积（AUC）作为性能指标评估性能。 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。

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.