Published on in Vol 24, No 6 (2022): June

This is a member publication of University of Cambridge (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37004, first published .
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation

Ting Dang 1, PhD;  Jing Han 1*, PhD;  Tong Xia 1*, MPhil;  Dimitris Spathis 1, PhD;  Erika Bondareva 1, MRES;  Chloë Siegele-Brown 1, PhD;  Jagmohan Chauhan 1, 2, PhD;  Andreas Grammenos 1, PhD;  Apinan Hasthanasombat 1, MPhil;  R Andres Floto 1, PhD;  Pietro Cicuta 1, PhD;  Cecilia Mascolo 1, PhD

1 Department of Computer Science and Technology, University of Cambridge , Cambridge , GB

2 Electronics and Computer Science, University of Southampton , Southampton , GB

*these authors contributed equally

Corresponding Author:

  • Ting Dang, PhD
  • Department of Computer Science and Technology
  • University of Cambridge
  • 15 JJ Thomson Ave
  • Cambridge
  • GB
  • Phone: 44 7895587796
  • Email: td464@cam.ac.uk