{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gadaleta M"],"funding":["U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences","U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS)","NCATS NIH HHS","National Science Foundation (NSF)","National Science Foundation"],"pagination":["166"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8655005"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["4(1)"],"pubmed_abstract":["Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC = 0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected."],"journal":["NPJ digital medicine"],"pubmed_title":["Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms."],"pmcid":["PMC8655005"],"funding_grant_id":["OIA-2040727","UL1 TR002550","UL1TR002550"],"pubmed_authors":["Quer G","Baca-Motes K","Topol EJ","Radin JM","Steinhubl SR","Gadaleta M","Kheterpal V","Ramos E"],"additional_accession":[]},"is_claimable":false,"name":"Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms.","description":"Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC = 0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Dec","modification":"2025-04-05T14:41:31.511Z","creation":"2025-04-05T14:41:31.511Z"},"accession":"S-EPMC8655005","cross_references":{"pubmed":["34880366"],"doi":["10.1038/s41746-021-00533-1"]}}