| Autor: |
Lorenz Risch, Marianna Mitratza, Paul Klaver, Harald Renz, Kirsten Grossmann, Corina Risch, Timo B. Brakenhoff, Martin Risch, Fiona Pereira, Ariel V. Dowling, George S. Downward, Billy Franks, Daniel Leibovitz, David Conen, Vladimir Kovacevic, Santiago Montes, Marc Kovac, Dorothea Hillmann, Raphael Twerenbold, Ornella C. Weideli, Maureen Cronin, Martina Rothenbühler, Stefanie Aeschbacher, Nadia Wohlwend, Diederick E. Grobbee, Brianna M. Goodale, Thomas Lung |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
SSRN Electronic Journal. |
| ISSN: |
1556-5068 |
| Popis: |
Background: We investigated machine learning based identification of the pre-symptomatic coronavirus disease 2019 (COVID-19) and detection of infection-related changes in physiology using a wearable device (the Ava bracelet). Methods: Participants from an ongoing cohort study (GAPP) of the general population in Liechtenstein were included in the current sub-study (COVI-GAPP). Nightly they wore the fertility bracelet that measured every ten seconds skin temperature, heart rate, respiratory rate, skin perfusion, and heart rate variability. Participants reported daily symptoms in a complementary app. Laboratory reverse transcription polymerase chain reaction (RT-PCR) and/or COVID-19 serology samples were collected from all participants. Long short-term memory (LSTM) based recurrent neural networks (RNN) were chosen for the binary classification of an individual as healthy or infected on a given day in a derivation and validation procedure. Findings: A total of 15 million hours of physiological data were recorded from 1163 participants (mean age 44 +/- 55 years). COVID-19 was confirmed in 127 participants. Of these, 66 (52%) had worn their device from baseline to symptom onset and were included in the analysis and RNN. Multi-level modelling revealed significantly different values in pre- versus post-symptomatic respiratory rate, temperature, heart rate, heart rate variability ratio, and skin perfusion. The developed RNN algorithm had a recall of 073 in the training set and 068 in the testing set (overall recall of 071) when detecting COVID-19 up to two days prior to symptom onset. Interpretation: Our proposed RNN algorithm identified 71% of COVID-19 positive participants two days prior to symptom onset. Wearable sensor technology can therefore enable COVID-19’ detection during the pre-symptomatic period. Funding: IMI grant agreement number 101005177, the Princely House of the Principality of Liechtenstein, the government of the Principality of Liechtenstein, and the Hanela Foundation in Switzerland. Declaration of Interest: Lorenz Risch, and Martin Risch are key shareholders of the Dr Risch Medical Laboratory. David Conen has received consulting fees from Roche Diagnostics, outside of the current work. The other authors have no financial or personal conflicts of interest to declare. Ethical Approval: The local ethics committee approved the study protocol, and written informed consent was obtained from each participant (BASEC 2020-00786). |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
