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

Mobile Health (mhealth) (3240) JMIR Theme Issue: COVID-19 Special Issue (2510) Artificial Intelligence (1838) Machine Learning (1825) Digital Biomarkers and Digital Phenotyping (239)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44410, first published .
Development and Validation of a Respiratory-Responsive Vocal Biomarker–Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19

Development and Validation of a Respiratory-Responsive Vocal Biomarker–Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19

Development and Validation of a Respiratory-Responsive Vocal Biomarker–Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19

Authors of this article:

Savneet Kaur1 Author Orcid Image ;   Erik Larsen2 Author Orcid Image ;   James Harper2 Author Orcid Image ;   Bharat Purandare3 Author Orcid Image ;   Ahmet Uluer4 Author Orcid Image ;   Mohammad Adrian Hasdianda4 Author Orcid Image ;   Nikita Arun Umale4 Author Orcid Image ;   James Killeen5 Author Orcid Image ;   Edward Castillo5 Author Orcid Image ;   Sunit Jariwala1 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

Journals

  1. Watase T, Omiya Y, Tokuno S. Severity Classification Using Dynamic Time Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study. JMIR Biomedical Engineering 2023;8:e50924 View
  2. Hu X, Lu J, Wang Y, Pang R, Liu J, Gou X, Bai X, Zhang A, Cheng H, Wang Q, Chang Y, Yin J, Chang C, Xiao H, Wang W. Effects of a lower limb walking exoskeleton on quality of life and activities of daily living in patients with complete spinal cord injury: A randomized controlled trial. Technology and Health Care 2024;32(1):243 View
  3. Narteni S, Baiardini I, Braido F, Mongelli M, Milanese M. Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients. PLOS ONE 2024;19(3):e0292980 View
  4. Chen Z, Liang N, Li H, Zhang H, Li H, Yan L, Hu Z, Chen Y, Zhang Y, Wang Y, Ke D, Shi N. Exploring explainable AI features in the vocal biomarkers of lung disease. Computers in Biology and Medicine 2024;179:108844 View
  5. Lin Y, Yan H, Lin C, Chang H. Identifying and Estimating Frailty Phenotypes by Vocal Biomarkers: Cross-Sectional Study. Journal of Medical Internet Research 2024;26:e58466 View
  6. Dawadi R, Inoue M, Tay J, Martin-Morales A, Vu T, Araki M. Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review. JMIR AI 2025;4:e59094 View
  7. Chen M, Li C, Yang Q, Zhang H, Zhang Y, Wang N, Dong J. Baseline type 2 biomarker levels and clinical remission predictors in children with asthma. Frontiers in Immunology 2025;16 View
  8. Zhang P, Ting H, Chang S. Using Cough Sounds for the Recognition of Multiclass Respiratory Diseases With Artificial Intelligence: A Review. IEEE Access 2025;13:114318 View

Books/Policy Documents

  1. Cascella M. Exploring AI in Pain Research and Management. View

Conference Proceedings

  1. Raj M, Malardhas J, Devapriya I. 2024 10th International Conference on Communication and Signal Processing (ICCSP). Machine Learning Approach To Predict Multiple Diseases Based On Symptoms View