Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
- Submitting institution
-
University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 10482
- Type
- E - Conference contribution
- DOI
-
10.1145/3394486.3412865
- Title of conference / published proceedings
- Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- First page
- 3474
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- August
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
8
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The database continues to receive contributions and part of it was shared over 150 times since it was made available. The UK and Italian Covid task forces have expressed interest in using this idea for pre-screening. In particular the UK Joint Bio Security Centre has relied on this work to set up the protocol for data collection and analysis of the UK audio data collection https://www.gov.uk/government/news/speak-up-and-help-beat-coronavirus-covid-19.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -