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

JMIR Theme Issue: COVID-19 Special Issue (2437) Mobile Health (mhealth) (2884) Machine Learning (1433) Hygiene and Infection Prevention (308) Clinical Information and Decision Making (1373) Artificial Intelligence (1323)

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
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