Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation.

Journal: Journal of medical Internet research
Published Date: Jun 21, 2022

Abstract

BACKGROUND: Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in health care systems.

Authors

  • Ting Dang
    Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
  • Jing Han
    Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education; School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China.
  • Tong Xia
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Dimitris Spathis
    Ionian University, Greece.
  • Erika Bondareva
  • Chloë Siegele-Brown
    Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
  • Jagmohan Chauhan
    Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
  • Andreas Grammenos
    Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
  • Apinan Hasthanasombat
    Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
  • R Andres Floto
    Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
  • Pietro Cicuta
    Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
  • Cecilia Mascolo

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