Cargando…
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
To identify Coronavirus disease (COVID-19) cases efficiently, affordably, and at scale, recent work has shown how audio (including cough, breathing and voice) based approaches can be used for testing. However, there is a lack of exploration of how biases and methodological decisions impact these too...
Autores principales: | Han, Jing, Xia, Tong, Spathis, Dimitris, Bondareva, Erika, Brown, Chloë, Chauhan, Jagmohan, Dang, Ting, Grammenos, Andreas, Hasthanasombat, Apinan, Floto, Andres, Cicuta, Pietro, Mascolo, Cecilia |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799654/ https://www.ncbi.nlm.nih.gov/pubmed/35091662 http://dx.doi.org/10.1038/s41746-021-00553-x |
Ejemplares similares
Ejemplares similares
-
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
por: Dang, Ting, et al.
Publicado: (2022) -
Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study
por: Han, Jing, et al.
Publicado: (2023) -
A summary of the ComParE COVID-19 challenges
por: Coppock, Harry, et al.
Publicado: (2023) -
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
por: Dang, Ting, et al.
Publicado: (2023) -
Laser-sound: optoacoustic transduction from digital audio streams
por: Kaleris, Konstantinos, et al.
Publicado: (2021)