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

JMIR Theme Issue: COVID-19 Special Issue (2244) Mobile Health (mhealth) (2242) Machine Learning (1122) Hygiene and Infection Prevention (260) Clinical Information and Decision Making (1046) Artificial Intelligence (829)

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 Dang 1 Author Orcid Image ;   Jing Han 1 Author Orcid Image ;   Tong Xia 1 Author Orcid Image ;   Dimitris Spathis 1 Author Orcid Image ;   Erika Bondareva 1 Author Orcid Image ;   Chloë Siegele-Brown 1 Author Orcid Image ;   Jagmohan Chauhan 1, 2 Author Orcid Image ;   Andreas Grammenos 1 Author Orcid Image ;   Apinan Hasthanasombat 1 Author Orcid Image ;   R Andres Floto 1 Author Orcid Image ;   Pietro Cicuta 1 Author Orcid Image ;   Cecilia Mascolo 1 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

Journals

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  2. Omiya Y, Mizuguchi D, Tokuno S. Distinguish the Severity of Illness Associated with Novel Coronavirus (COVID-19) Infection via Sustained Vowel Speech Features. International Journal of Environmental Research and Public Health 2023;20(4):3415 View
  3. Tolmachev I, Kaverina I, Vrazhnov D, Starikov I, Starikova E, Kostuchenko E. Application of Artificial Intelligence Methods Depending on the Tasks Solved during COVID-19 Pandemic. COVID 2022;2(10):1341 View
  4. Chen Y, Mascolo C. Women in Networks: Professor Cecilia Mascolo. IEEE Network 2022;36(4):4 View
  5. Ayappan G, Anila S. Mayfly Optimization with Deep Belief Network-Based Automated COVID-19 Cough Classification Using Biological Audio Signals. Cybernetics and Systems 2023:1 View
  6. Xia T, Han J, Mascolo C. Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues. Experimental Biology and Medicine 2022;247(22):2053 View
  7. Matias P, Costa J, Carreiro A, Gamboa H, Sousa I, Gomez P, Sousa J, Neuparth N, Carreiro-Martins P, Soares F. Clinically Relevant Sound-Based Features in COVID-19 Identification: Robustness Assessment With a Data-Centric Machine Learning Pipeline. IEEE Access 2022;10:105149 View
  8. Shen J, Ghatti S, Levkov N, Shen H, Sen T, Rheuban K, Enfield K, Facteau N, Engel G, Dowdell K. A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine. Frontiers in Artificial Intelligence 2022;5 View
  9. Sarmiento Varón L, González-Puelma J, Medina-Ortiz D, Aldridge J, Alvarez-Saravia D, Uribe-Paredes R, Navarrete M. The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management. Frontiers in Public Health 2023;11 View