NIH teams with MIT LL to launch a data platform to leverage nationwide wearable datasets to enable early detection of COVID-19 and other respiratory illness

The National Institutes of Health have partnered with the MIT Lincoln Laboratory to launch a secure data platform populated with large curated and validated datasets focused on digital health technologies (including wearable device data) and their ability to help detect COVID-19 and other respiratory infections. 

The Rapid AI Platform for Innovating Data Science (RAPIDS) platform focuses on housing digital health data paired with physiological data, curated to FAIR principles to ensure data understandability and facilitate reuse, to advance AI model development in the fight against emerging biological threats. RAPIDS is funded by multiple government agencies, including NIH, DTRA and BARDA. The initiative will create an analytics platform to systematically collect physiological data from researchers, harmonize that data to a common standard, and provide tools to allow researchers to create their own cross-study cohort datasets to expand the reach and robustness of their research efforts. The effort will also enable the creation, advancement, storage, and reuse of advanced analytical models to leverage AI advancements in the fight against emerging infectious diseases.

Currently the platform contains 9 studies conducted under NIH Digital Health Solutions for COVID-19 initiative and has longitudinal physiological data for over 160,000 individuals collected over a period of 3 to 12 months. The primary exposure for these datasets is SARS-CoV-2, while some studies focusing on influenza exposure, as well as behavioral data.

The platform is accessible by the public at rapids.ll.mit.edu.