  The world has been affected by COVID-19 coronavirus. At the time of this
study, the number of infected people in the United States is the highest
globally (7.9 million infections). Within the infected population, patients
diagnosed with acute respiratory distress syndrome (ARDS) are in more
life-threatening circumstances, resulting in severe respiratory system failure.
Various studies have investigated the infections to COVID-19 and ARDS by
monitoring laboratory metrics and symptoms. Unfortunately, these methods are
merely limited to clinical settings, and symptom-based methods are shown to be
ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to
early-detect different respiratory diseases in ubiquitous health monitoring. We
posit that such biomarkers are informative in identifying ARDS patients
infected with COVID-19. In this study, we investigate the behavior of COVID-19
on ARDS patients by utilizing simple vital signs. We analyze the long-term
daily logs of blood pressure and heart rate associated with 70 ARDS patients
admitted to five University of California academic health centers (containing
42506 samples for each vital sign) to distinguish subjects with COVID-19
positive and negative test results. In addition to the statistical analysis, we
develop a deep neural network model to extract features from the longitudinal
data. Using only the first eight days of the data, our deep learning model is
able to achieve 78.79% accuracy to classify the vital signs of ARDS patients
infected with COVID-19 versus other ARDS diagnosed patients.
