Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app

@article{Sudre2021SymptomCI,
  title={Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app},
  author={Carole H. Sudre and KarlaA Lee and Mary Ni Lochlainn and Thomas Varsavsky and Benjamin Murray and Mark S. Graham and Cristina Menni and Marc Modat and Ruth C. E. Bowyer and Long H. Nguyen and David A. Drew and Amit D. Joshi and Wenjie Ma and Chuan-Guo Guo and Chun-Han Lo and Sajaysurya Ganesh and Abubakar Buwe and Joan Capdevila Pujol and Julien Lavigne du Cadet and Alessia Visconti and Maxim B. Freidin and Julia Sarah El-sayed Moustafa and Mario Falchi and Richard Davies and Maria F. Gomez and Tove Fall and Manuel Jorge Cardoso and Jonathan Wolf and Paul W. Franks and Andrew T. Chan and Tim D. Spector and Claire J. Steves and S{\'e}bastien Ourselin},
  journal={Science Advances},
  year={2021},
  volume={7}
}
Longitudinal clustering of symptoms can predict the need for respiratory support in severe COVID-19. As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom… 

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