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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

More details can be found in Multimedia Appendix 1 and Xia et al [28]. From April 2020 to April 2021 (Figure 3a), 3845 healthy participants (COVID-19–negative) and 1456 COVID-19–positive participants contributed audio samples with positive or negative clinical self-reported test results for at least 1 day. We used these participants' data if 5 or more samples were provided, resulting in 447 (11.6%) and 168 (11.5%) negatively and positively tested participants, respectively.

Ting Dang, Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, R Andres Floto, Pietro Cicuta, Cecilia Mascolo

J Med Internet Res 2022;24(6):e37004