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

JMIR Theme Issue: COVID-19 Special Issue (2548) Mobile Health (mhealth) (3440) Machine Learning (2051) Hygiene and Infection Prevention (372) Clinical Information and Decision Making (1893) Artificial Intelligence (2088)

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 (28) Tweetations (8) Metrics

Journals

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  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
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  15. Soprano M, Roitero K, Gadiraju U, Maddalena E, Demartini G. Longitudinal Loyalty: Understanding The Barriers To Running Longitudinal Studies On Crowdsourcing Platforms. ACM Transactions on Social Computing 2024;7(1-4):1 View
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  17. Berendse S, Krabbe J, Klaus J, Ahmed F. Towards Explainable Machine Learning for Prediction of Disease Progression. Applied Artificial Intelligence 2024;38(1) View
  18. Cai J, Wang R, Zhao D, Yuan Z, McKenna V, Friedman A, Foot R, Storey S, Boente R, Vhaduri S, Min B. Multimodal Audio-Based Disease Prediction With Transformer-Based Hierarchical Fusion Network. IEEE Transactions on Audio, Speech and Language Processing 2025;33:1170 View
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Books/Policy Documents

  1. Bhidayasiri R, Goetz C. Handbook of Digital Technologies in Movement Disorders. View

Conference Proceedings

  1. Dang T, Han J, Xia T, Bondareva E, Siegele-Brown C, Chauhan J, Grammenos A, Spathis D, Cicuta P, Mascolo C. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Conditional Neural ODE Processes for Individual Disease Progression Forecasting: A Case Study on COVID-19 View
  2. Manikandan P, Swedheetha C, Venkatakrishna P, Kasireddy K, Reddy N, Kaushik T. 2025 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). Respiratory Disease Diagnosis Reporting System Using CNN and LSTM Machine Learning Algorithms View