ELECTRONIC HEALTH RECORD BIG DATA AND RADIOMIC ANALYTICS FOR PRECISION MEDICINE APPROACH TO LONG-COVID

There is a growing recognition that a large proportion of SARS-CoV-2-infected individuals continue to experience a broad range of symptoms after recovering from the initial bout of the COVID-19 illness. These patients are colloquially referred to as “COVID long haulers,” and the illness as “Long COVID.” There are over 35 million COVID survivors in U.S. and 3.5 million estimated in Texas. Long COVID is a national and even a global public health issue. The NIH recently gave “Long COVID” a formal name, Post-Acute Sequelae of SARs-CoV-2 infection (PASC), but has not yet formally characterized the illness; this highlights the substantial knowledge gap between the widespread prevalence of this public health issue and its understanding in the medical community.

This project is a collaborative effort between Southwest Research Institute, UT Health San Antonio (UTHSA), and UT San Antonio. The project will use the UTHSA COVID-19 Infectious Diseases Outpatient Clinic cohort (>12,000 patients diagnosed with acute COVID-19) by systematic deep phenotyping of clinical characteristics from different data sources (data warehouse, clinical chart review, unstructured clinical and radiology notes using NLP, and patient-reported symptoms). The study aims to correlate PASC symptoms with radiomic and radiopathomic information by analyzing image data using Computer Vision artificial intelligence and machine learning. The outcome of this project will be a predictive tool via turnkey implementation of an EMR-based application for quick scaling across most academic and many non-academic clinical settings.