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

Journals

  1. Sobahi N, Atila O, Deniz E, Sengur A, Acharya U. Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds. Biocybernetics and Biomedical Engineering 2022;42(3):1066 View
  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;54(6):767 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
  10. Triantafyllopoulos A, Semertzidou A, Song M, Pokorny F, Schuller B. Introducing the COVID-19 YouTube (COVYT) speech dataset featuring the same speakers with and without infection. Biomedical Signal Processing and Control 2024;88:105642 View
  11. Dang T, Spathis D, Ghosh A, Mascolo C. Human-centred artificial intelligence for mobile health sensing: challenges and opportunities. Royal Society Open Science 2023;10(11) View
  12. Idrisoglu A, Dallora A, Anderberg P, Berglund J. Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. Journal of Medical Internet Research 2023;25:e46105 View
  13. Saeed T, Ijaz A, Sadiq I, Qureshi H, Rizwan A, Imran A. An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio. Bioengineering 2024;11(1):55 View
  14. Ershadi M, Rise Z. Uncertain SEIAR system dynamics modeling for improved community health management of respiratory virus diseases: A COVID-19 case study. Heliyon 2024;10(3):e24711 View
  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 View

Books/Policy Documents

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