An infection prediction model developed from inpatient data can predict out-of-hospital COVID-19 infections from wearable data when controlled for dataset shift

• Published in: Scientific reports Vol. 15; no. 1; pp. 33367 - 13
• Main Authors: Feng, Ting, Mariani, Sara, Conroy, Bryan, Damiano, Robert, Silva, Ikaro, Swearingen, Dennis, McFarlane, Daniel C.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.09.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/699/255/2514; 692/700/459; 692/700/478; Algorithms; Artificial intelligence; Clinical decision support (CDS); COVID-19; COVID-19 - diagnosis; COVID-19 - epidemiology; COVID-19 infection; Critical care; Cross Infection - diagnosis; Data collection; Dataset shift; Datasets; Demographics; Diagnostic tests; Disease transmission; Epidemics; Female; Heart rate; Hospitals; Humanities and Social Sciences; Humans; Infection prediction; Infectious diseases; Inpatients; Learning algorithms; Machine Learning; Male; Middle Aged; Monitoring systems; multidisciplinary; Nosocomial infection; Nosocomial infections; Original Research; Outbreaks; Pandemics; Patients; Physiology; Prediction models; Probability distribution; Public health; Review boards; SARS-CoV-2 - isolation & purification; Science; Science (multidisciplinary); Wearable computers; Wearable Electronic Devices; Wearable physiological monitoring; United States > US; Dataset shift; COVID-19 infection; Infection prediction; Clinical decision support (CDS); Machine learning; Wearable physiological monitoring
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-17593-y

The COVID-19 pandemic highlighted the importance of early detection of illness and the need for health monitoring solutions outside of the hospital setting. We have previously demonstrated a real-time system to identify COVID-19 infection before diagnostic testing, that was powered by commercial-off-the-shelf wearables and machine learning models trained with wearable physiological data from COVID-19 cases outside of hospitals. However, these types of solutions were not readily available at the onset nor during the early outbreak of a new infectious disease when preventing infection transmission was critical, due to a lack of pathogen-specific illness data to train the machine learning models. This study investigated whether a pretrained clinical decision support algorithm for predicting hospital-acquired infection (predating COVID-19) could be readily adapted to detect early signs of COVID-19 infection from wearable physiological signals collected in an unconstrained out-of-hospital setting. A baseline comparison where the pretrained model was applied directly to the wearable physiological data resulted a performance of AUROC = 0.52 in predicting COVID-19 infection. After controlling for contextual effects and applying an unsupervised dataset shift transformation derived from a small set of wearable data from healthy individuals, we found that the model performance improved, achieving an AUROC of 0.74, and it detected COVID-19 infection on average 2 days prior to diagnostic testing. Our results suggest that it is possible to deploy a wearable physiological monitoring system with an infection prediction model pretrained from inpatient data, to readily detect out-of-hospital illness at the emergence of a new infectious disease outbreak.