Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers.

ABSTRACT: Conventional testing and diagnostic methods for infections like SARS-CoV-2 have limitations for population health management and public policy. We hypothesize that daily changes in autonomic activity, measured through off-the-shelf technologies together with app-based cognitive assessments, may be used to forecast the onset of symptoms consistent with a viral illness. We describe our strategy using an AI model that can predict, with 82% accuracy (negative predictive value 97%, specificity 83%, sensitivity 79%, precision 34%), the likelihood of developing symptoms consistent with a viral infection three days before symptom onset. The model correctly predicts, almost all of the time (97%), individuals who will not develop viral-like illness symptoms in the next three days. Conversely, the mo

SUBMITTER: D'Haese PF 

PROVIDER: S-EPMC8516235 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature