Machine learning COVID-19 detection from wearables

Affiliations

¹ Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Vector Institute, Toronto, ON, Canada; Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
² Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
³ Evidation Health, San Mateo, CA, USA.
⁴ Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA.
⁵ Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
⁶ Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Vector Institute, Toronto, ON, Canada; Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁷ Evidation Health, San Mateo, CA, USA; Sage Bionetworks, Seattle, WA, USA.
⁸ Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Vector Institute, Toronto, ON, Canada; Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; CIFAR, Toronto, ON, Canada. Electronic address: anna.goldenberg@utoronto.ca.

PMID: 36963907
PMCID: PMC10032660
DOI: 10.1016/S2589-7500(23)00045-6

Machine learning COVID-19 detection from wearables

Bret Nestor et al. Lancet Digit Health. 2023 Apr.

. 2023 Apr;5(4):e182-e184.

doi: 10.1016/S2589-7500(23)00045-6.

Authors

Affiliations

¹ Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Vector Institute, Toronto, ON, Canada; Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
² Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
³ Evidation Health, San Mateo, CA, USA.
⁴ Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA.
⁵ Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
⁶ Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Vector Institute, Toronto, ON, Canada; Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁷ Evidation Health, San Mateo, CA, USA; Sage Bionetworks, Seattle, WA, USA.
⁸ Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Vector Institute, Toronto, ON, Canada; Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; CIFAR, Toronto, ON, Canada. Electronic address: anna.goldenberg@utoronto.ca.

PMID: 36963907
PMCID: PMC10032660
DOI: 10.1016/S2589-7500(23)00045-6

No abstract available

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Conflict of interest statement

LF is a co-founder of Evidation Health, a company that powers research studies with person-generated health data. This work was funded by the National Institutes of Health, the National Cancer Institute, and the National Institute of Biomedical Imaging and Bioengineering award N. 75N91020C00034. AG is also funded by the Varma Family Chair and CIFAR AI Chair.

Figures

**Figure**
Reported AUROCs by COVID-19 prevalence in the test set COVID-19 prevalence is defined as the ratio of COVID-19 symptom days to all days. The dashed line shows the average prevalence from March 15, 2020, to Dec 31, 2020, in the USA, as reported by Pei and colleagues. Our model is evaluated on three sets of the prospective testing data (FLUVEY): all available data, the subset of patients who report having COVID-19, and the subset of patients reporting illnesses on days 0–3 of symptom onset. Other data are from Mason et al, Merrill et al, Natarajan et al, Quer et al, and Shandhi et al. AUROC=area under the receiver operating characteristic curve.

See this image and copyright information in PMC

References

1. Radin JM, Wineinger NE, Topol EJ, Steinhubl SR. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digit Health. 2020;2:e85–e93. - PMC - PubMed
1. Hirten RP, Danieletto M, Tomalin L, et al. Use of physiological data from a wearable device to identify SARS-CoV-2 infection and symptoms and predict COVID-19 diagnosis: observational study. J Med Internet Res. 2021;23 - PMC - PubMed
1. Mason AE, Hecht FM, Davis SK, et al. Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict study. Sci Rep. 2022;12 - PMC - PubMed
1. Mishra T, Wang M, Metwally AA, et al. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng. 2020;4:1208–1220. - PMC - PubMed
1. Quer G, Radin JM, Gadaleta M, et al. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat Med. 2021;27:73–77. - PubMed

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Machine learning COVID-19 detection from wearables

Affiliations

Machine learning COVID-19 detection from wearables

Authors

Affiliations

Conflict of interest statement

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References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources