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

JMIR Theme Issue: COVID-19 Special Issue (2452) Theme Issue 2022: Chatbots and COVID-19 (42) Clinical Information and Decision Making (1407) Machine Learning (1465) Artificial Intelligence (1371) New Methods (944)

Published on in Vol 25 (2023)

This is a member publication of University of Cambridge (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44804, first published .
Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study

Authors of this article:

Jing Han1 Author Orcid Image ;   Marco Montagna2 Author Orcid Image ;   Andreas Grammenos1 Author Orcid Image ;   Tong Xia1 Author Orcid Image ;   Erika Bondareva1 Author Orcid Image ;   Chloë Siegele-Brown3 Author Orcid Image ;   Jagmohan Chauhan3 Author Orcid Image ;   Ting Dang1 Author Orcid Image ;   Dimitris Spathis1 Author Orcid Image ;   R Andres Floto4 Author Orcid Image ;   Pietro Cicuta5 Author Orcid Image ;   Cecilia Mascolo1 Author Orcid Image
Article Authors Cited by (2) Tweetations (9) Metrics

Jing Han   1 , PhD ;   Marco Montagna   2 , MD ;   Andreas Grammenos   1 , PhD ;   Tong Xia   1 , MSc ;   Erika Bondareva   1 , MRes ;   Chloë Siegele-Brown   3 , PhD ;   Jagmohan Chauhan   3 , PhD ;   Ting Dang   1 , PhD ;   Dimitris Spathis   1 , PhD ;   R Andres Floto   4 , Prof Dr ;   Pietro Cicuta   5 , Prof Dr ;   Cecilia Mascolo   1 , Prof Dr

1 Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom

2 Department of Medicine, Vita-Salute San Raffaele University, Milan, Italy

3 Electronics and Computer Science, University of Southampton, Southampton, United Kingdom

4 Department of Medicine, University of Cambridge, Cambridge, United Kingdom

5 Department of Physics, University of Cambridge, Cambridge, United Kingdom

Corresponding Author:

  • Jing Han, PhD
  • Department of Computer Science and Technology
  • University of Cambridge
  • 15 JJ Thomson Ave
  • Cambridge, CB3 0FD
  • United Kingdom
  • Phone: 44 012237 ext 63540
  • Email: jh2298@cam.ac.uk