This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2452) Mobile Health (mhealth) (2939) Machine Learning (1465) Hygiene and Infection Prevention (317) Clinical Information and Decision Making (1407) Artificial Intelligence (1371)

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

Authors of this article:

Ting Dang1 Author Orcid Image ;   Jing Han1 Author Orcid Image ;   Tong Xia1 Author Orcid Image ;   Dimitris Spathis1 Author Orcid Image ;   Erika Bondareva1 Author Orcid Image ;   Chloë Siegele-Brown1 Author Orcid Image ;   Jagmohan Chauhan1, 2 Author Orcid Image ;   Andreas Grammenos1 Author Orcid Image ;   Apinan Hasthanasombat1 Author Orcid Image ;   R Andres Floto1 Author Orcid Image ;   Pietro Cicuta1 Author Orcid Image ;   Cecilia Mascolo1 Author Orcid Image
Article Authors Cited by (19) Tweetations (8) Metrics

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, United Kingdom

2 Electronics and Computer Science, University of Southampton, Southampton, United Kingdom

*these authors contributed equally

Corresponding Author:

  • Ting Dang, PhD
  • Department of Computer Science and Technology
  • University of Cambridge
  • 15 JJ Thomson Ave
  • Cambridge, CB3 0FD
  • United Kingdom
  • Phone: 44 7895587796
  • Email: td464@cam.ac.uk