Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis
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Abstract
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9–89.9%) of subjects ( n = 86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4–96.3%) of individuals ( n = 78). The diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0–87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.
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Other examples of virtual health coaches have tackled common conditions such as migraine, asthma and chronic obstructive pulmonary disease.
“Other examples of virtual health coaches have tackled common conditions such as migraine, asthma and chronic obstructive pulmonary disease, among others 76 .”
From Multimodal biomedical AI (2022) · cites this paper as [76]
The Methods describe a smartphone-based diagnostic algorithm for COPD exacerbations and mention acute asthma among the conditions targeted by the broader program. The Discussion describes possible use in telehealth or self-management, but does not describe the algorithm as a virtual health coach; the paper also provides no example involving migraine. No page numbers were available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMost of these applications have been tested only on small observational studies, and much more research, including randomized clinical trials, are needed to evaluate their benefits.
“Unfortunately, most of these applications have been tested only on small observational studies, and much more research, including randomized clinical trials, are needed to evaluate their benefits.”
From Multimodal biomedical AI (2022) · cites this paper as [76]
The Discussion (page unavailable) describes this app’s evaluation as a single-site clinical study and identifies limits to its generalizability and at-home use. That supports caution about this application, but the paper does not establish that most applications have been tested only in small observational studies or call specifically for randomized clinical trials to evaluate their benefits. (LLM, conf=0.83)
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Cites
Vestbo, J., Hurd, S. S., Agustí, A. G., Jones, P. W., Vogelmeier, C., Anzueto, A., Barnes, P. J., Fabbri, L. M., Martinez, F. J., Nishimura, M., et al. (2013) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201204-0596pp · Cited by 15,983 · see citing works
Miller, M. R., Hankinson, J., Brusasco, V., Burgos, F., Casaburi, R., Coates, A., Crapo, R., Enright, P., van der Grinten, C. P. M., Gustafsson, P., et al. (2005) · European Respiratory Journal · doi:10.1183/09031936.05.00034805 · Cited by 15,627 · see citing works
Standardization of Spirometry — 1987 Update
Buist, A. S. (1987) · American Review of Respiratory Disease · doi:10.1164/ajrccm/136.5.1073 · Cited by 3,275 · see citing works
Global and regional estimates of COPD prevalence: Systematic review and meta–analysis
Adeloye, D., Chua, S., Lee, C., Basquill, C., Papana, A., Τheodoratou, E., Nair, H., Gašević, D., Sridhar, D., Campbell, H. J., et al. (2015) · Journal of Global Health · doi:10.7189/jogh.05.020415 · Cited by 1,266 · see citing works
Early Therapy Improves Outcomes of Exacerbations of Chronic Obstructive Pulmonary Disease
Wilkinson, T. M. A., Donaldson, G. C., Hurst, J. R., Seemungal, T., & Wedzicha, J. A. (2004) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200310-1443oc · Cited by 683 · see citing works
Lenferink, A., Brusse-Keizer, M., van der Valk, P. D., Frith, P. A., Zwerink, M., Monninkhof, E. M., van der Palen, J., & Effing, T. W. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011682.pub2 · Cited by 328 · see citing works
Underreporting Exacerbation of Chronic Obstructive Pulmonary Disease in a Longitudinal Cohort
Langsetmo, L., Platt, R. W., Ernst, P., & Bourbeau, J. (2007) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200708-1290oc · Cited by 321 · see citing works
Patient Understanding, Detection, and Experience of COPD Exacerbations
Kessler, R., Ståhl, E., Vogelmeier, C. F., Haughney, J., Trudeau, E., Löfdahl, C., & Partridge, M. R. (2006) · CHEST Journal · doi:10.1378/chest.130.1.133 · Cited by 255 · see citing works
Gurung, A., Scrafford, C., Tielsch, J. M., Levine, O. S., & Checkley, W. (2011) · Respiratory Medicine · doi:10.1016/j.rmed.2011.05.007 · Cited by 228 · see citing works
Pasquale, M. K., Sun, Сонг, Hartnett, & Stemkowski (2012) · International Journal of COPD · doi:10.2147/copd.s36997 · Cited by 168 · see citing works
Effects of written action plan adherence on COPD exacerbation recovery
Bischoff, E. W. M. A., Hamd, D. H., Sedeno, M. F., Benedetti, A., Schermer, T. R., Bernard, S., Maltais, F., & Bourbeau, J. (2010) · Thorax · doi:10.1136/thx.2009.127621 · Cited by 158 · see citing works
Porter, P., Abeyratne, U. R., Swarnkar, V. R., Tan, J., Ng, T.-W., Brisbane, J. M., Speldewinde, D., Choveaux, J., Sharan, R. V., Kosasih, K., et al. (2019) · Respiratory Research · doi:10.1186/s12931-019-1046-6 · Cited by 139 · see citing works
Self-citationWavelet Augmented Cough Analysis for Rapid Childhood Pneumonia Diagnosis
Kosasih, K., Abeyratne, U. R., Swarnkar, V. R., & Triasih, R. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2381214 · Cited by 123 · see citing works
Self-citationVorrink, S., Kort, H. S. M., Troosters, T., Zanen, P., & Lammers, J. J. (2016) · European Respiratory Journal · doi:10.1183/13993003.00083-2016 · Cited by 111 · see citing works
Cough Sound Analysis Can Rapidly Diagnose Childhood Pneumonia
Abeyratne, U. R., Swarnkar, V. R., Setyati, A., & Triasih, R. (2013) · Annals of Biomedical Engineering · doi:10.1007/s10439-013-0836-0 · Cited by 109 · see citing works
Self-citationCOPD management costs according to the frequency of COPD exacerbations in UK primary care
Punekar, Y. S., Shukla, A., & Müllerová, H. (2014) · International Journal of COPD · doi:10.2147/copd.s54417 · Cited by 107 · see citing works
Automatic Croup Diagnosis Using Cough Sound Recognition
Sharan, R. V., Abeyratne, U. R., Swarnkar, V. R., & Porter, P. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2849502 · Cited by 89 · see citing works
Self-citationAutomatic cough segmentation from non-contact sound recordings in pediatric wards
Amrulloh, Y. A., Abeyratne, U. R., Swarnkar, V. R., Triasih, R., & Setyati, A. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.05.001 · Cited by 72 · see citing works
Self-citationComputer-aided diagnosis of pneumonia in patients with chronic obstructive pulmonary disease
Morillo, D., Jiménez, A. L., & Moreno, S. A. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001171 · Cited by 69 · see citing works
Patients’ experience of identifying and managing exacerbations in COPD: a qualitative study
Williams, V., Hardinge, M., Ryan, S., & Farmer, A. A. (2014) · npj Primary Care Respiratory Medicine · doi:10.1038/npjpcrm.2014.62 · Cited by 66 · see citing works
Remote daily real-time monitoring in patients with COPD – A feasibility study using a novel device
Sund, Z. M., Powell, T., Greenwood, R., & Jarad, N. A. (2009) · Respiratory Medicine · doi:10.1016/j.rmed.2009.03.017 · Cited by 58 · see citing works
Signal feature extraction by multi-scale PCA and its application to respiratory sound classification
Xie, S., Feng, J., Krishnan, S., & Sattar, F. (2012) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-012-0903-y · Cited by 50 · see citing works
Predicting spirometry readings using cough sound features and regression
Sharan, R. V., Abeyratne, U. R., Swarnkar, V. R., Claxton, S., Hukins, C., & Porter, P. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aad948 · Cited by 43 · see citing works
Self-citationDetecting exacerbations using the Clinical COPD Questionnaire
Trappenburg, J. C. A., Bos-Touwen, I. D., Oene, G. H. D. W.-V., Bourbeau, J., Monninkhof, E. M., Verheij, T. J., Lammers, J. J., & Schrijvers, G. (2010) · Health and Quality of Life Outcomes · doi:10.1186/1477-7525-8-102 · Cited by 41 · see citing works
Acute Exacerbations of COPD: Delay in Presentation and the Risk of Hospitalization
Chandra, D., Tsai, C., & Camargo, C. A. (2009) · COPD Journal of Chronic Obstructive Pulmonary Disease · doi:10.1080/15412550902751746 · Cited by 38 · see citing works
Edwards, L., Perrin, K., Wijesinghe, M., Weatherall, M., Beasley, R. W., & Travers, J. (2011) · Respirology · doi:10.1111/j.1440-1843.2011.01926.x · Cited by 33 · see citing works
Neural network based algorithm for automatic identification of cough sounds
Swarnkar, V. R., Abeyratne, U. R., Amrulloh, Y. A., Hukins, C., Triasih, R., & Setyati, A. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6609862 · Cited by 30 · see citing works
Self-citationCough sound analysis for diagnosing croup in pediatric patients using biologically inspired features
Sharan, R. V., Abeyratne, U. R., Swarnkar, V. R., & Porter, P. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037875 · Cited by 22 · see citing works
Self-citationPorter, P., Brisbane, J. M., Abeyratne, U. R., Bear, N., Wood, J., Peltonen, V., Della, P. R., Smith, C., & Claxton, S. (2020) · British Journal of General Practice · doi:10.3399/bjgp.2020.0750 · Cited by 22 · see citing works
Self-citationStratifying asthma severity in children using cough sound analytic technology
Swarnkar, V. R., Abeyratne, U. R., Tan, J., Ng, T.-W., Brisbane, J. M., Choveaux, J., & Porter, P. (2019) · Journal of Asthma · doi:10.1080/02770903.2019.1684516 · Cited by 20 · see citing works
Self-citationPorter, P., Brisbane, J. M., Abeyratne, U. R., Bear, N., Wood, J., Peltonen, V., Della, P. R., Purdie, F., Smith, C., & Claxton, S. (2020) · JMIR Formative Research · doi:10.2196/24587 · Cited by 20 · see citing works
Self-citationHow should we define and classify exacerbations in chronic obstructive pulmonary disease?
Trigueros, J. A. (2013) · Expert Review of Respiratory Medicine · doi:10.1586/ers.13.16 · Cited by 12 · see citing works
Classification of Wheeze Sounds Using Cepstral Analysis and Neural Networks
Hashemi, A., Arabalibeik, H., & Agin, K. (2012) · Studies in health technology and informatics · doi:10.3233/978-1-61499-022-2-161 · Cited by 11 · see citing works
Telehealthcare in COPD – A feasibility trial
Rassouli, F., Pfister, M., Baty, F., Widmer, S., & Brutsche, M. H. (2016) · doi:10.1183/13993003.congress-2016.pa1019 · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 35 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · see citing works
Phenotypes, Etiotypes, and Endotypes of Exacerbations of Chronic Obstructive Pulmonary Disease
Bhatt, S. P., Agustí, A. G. N., Bafadhel, M., Christenson, S. A., Bon, J. M., Donaldson, G. C., Sin, D. D., Wedzicha, J. A., & Martínez, F. J. (2023) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202209-1748so · Cited by 86 · see citing works
Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review
Kapetanidis, P., Kalioras, F., Tsakonas, C. G., Tzamalis, P., Kontogiannis, G., Karamanidou, T., Stavropoulos, T. G., & Nikoletseas, S. (2024) · Sensors · doi:10.3390/s24041173 · Cited by 77 · see citing works
Risk of Death and Cardiovascular Events Following an Exacerbation of COPD: The EXACOS-CV US Study
Daniels, K. M., Lanes, S., Tave, A. K., Pollack, M. F., Mannino, D. M., Criner, G. J., Neikirk, A., Rhodes, K. M., Feigler, N., & Nordon, C. (2024) · International Journal of COPD · doi:10.2147/copd.s438893 · Cited by 54 · see citing works
New insights into crosstalk between Nrf2 pathway and ferroptosis in lung disease
Chen, Y., Jiang, Z., & Li, X. (2024) · Cell Death and Disease · doi:10.1038/s41419-024-07224-1 · Cited by 57 · see citing works
Alotaibi, B. S., Negm, W. A., Elekhnawy, E., El-Masry, T. A., Elseady, W. S., Saleh, A., Alotaibi, K. N., & El-Sherbeni, S. A. (2021) · Antibiotics · doi:10.3390/antibiotics10121444 · Cited by 44 · see citing works
Chronic cough—the limitation and advances in assessment techniques
Zhang, M., Sykes, D. L., Brindle, K., Sadofsky, L. R., & Morice, A. H. (2022) · Journal of Thoracic Disease · doi:10.21037/jtd-22-874 · Cited by 36 · see citing works
Early Identification of Exacerbations in Patients with Chronic Obstructive Pulmonary Disease (COPD)
Voulgareli, I., Antonogiannaki, E.-M., Bartziokas, Κ., Zaneli, S., Bakakos, P., Loukides, S., & Papaioannou, A. Ι. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020397 · Cited by 18 · see citing works
Landry, V., Matschek, J., Pang, R., Munipalle, M., Tan, K., Boruff, J. T., & Li‐Jessen, N. Y. K. (2025) · European Respiratory Review · doi:10.1183/16000617.0246-2024 · Cited by 21 · see citing works
Fagherazzi, G., Zhang, L., Elbéji, A., Higa, E., Despotović, V., Ollert, M. W., Aguayo, G., Nazarov, P. V., & Fischer, A. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000112 · Cited by 10 · see citing works
Smart Devices Are Poised to Revolutionize the Usefulness of Respiratory Sounds
Kraman, S. S., Pasterkamp, H., & Wodicka, G. R. (2023) · CHEST Journal · doi:10.1016/j.chest.2023.01.024 · Cited by 10 · see citing works
HEAR4Health: a blueprint for making computer audition a staple of modern healthcare
Triantafyllopoulos, A., Kathan, A., Baird, A., Christ, L., Gebhard, A., Gerczuk, M., Karas, V., Hübner, T., Jing, X., Liu, S., et al. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1196079 · Cited by 10 · see citing works
Porter, P., Brisbane, J. M., Abeyratne, U. R., Bear, N., & Claxton, S. (2022) · Journal of Asthma · doi:10.1080/02770903.2022.2051546 · Cited by 8 · see citing works
Self-citationYin, Y., Xu, J., Cai, S., Chen, Y., Chen, Y., Li, M., Zhang, Z., & Kang, J. (2022) · International Journal of COPD · doi:10.2147/copd.s363935 · Cited by 7 · see citing works
Gong, Y., Xu, C., Mo, C., Wu, J., Lin, H., Su, H., Zhang, Q., Zhang, Q., & Yang, S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02086-z · Cited by 6 · see citing works
Novel Deep Learning Approaches to Differentiate Asthma and COPD Based on Cough Sounds
Vodnala, N., Yarlagadda, P. S., Sri Phani Krishna, K., Ch, M., & Sailaja, K. (2024) · Parul International Conference on Engineering and Technology (PICET) · doi:10.1109/picet60765.2024.10716083 · Cited by 6 · see citing works
Dang, T. B., Nguyen, C. C., Heo, S. Y., Nguyen, T. V., Tong, N., Ruppert, M. G., Truong, T. A., Listyawan, M. A., Davies, J., Zhao, S., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73636-6 · Cited by 5 · see citing works
Du, C., Zhou, J. T., & Yu, Y. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1608266 · Cited by 4 · see citing works
Voice as a Health Indicator: The Use of Sound Analysis and AI for Monitoring Respiratory Function
Lentz-Nielsen, N., Maaløe, L., Madeleine, P., & Blomberg, S. N. F. (2025) · BioMedInformatics · doi:10.3390/biomedinformatics5020031 · Cited by 3 · see citing works
Cao, Y., Zhao, X., Yang, Y., Zhu, S., Zheng, L.-D., Ying, T., Sha, Z., Zhu, R., & Wu, T. (2023) · Geriatric Nursing · doi:10.1016/j.gerinurse.2023.02.007 · Cited by 3 · see citing works
Hart, J., Edwards, A. D., & Stainthorpe, A. (2024) · Frontiers in Health Services · doi:10.3389/frhs.2023.1302653 · Cited by 2 · see citing works
Deep Learning Model for Early Subsequent COPD Exacerbation Prediction
Abineza, C., Bălaş, V. E., & Nsengiyumva, P. (2023) · Studies in Informatics and Control · doi:10.24846/v32i3y202309 · Cited by 1 · see citing works
CoughViT: A Self-Supervised Vision Transformer for Cough Audio Representation Learning
Luong, J., Xue, H., & Salim, F. D. (2025) · ACM International Symposium on Wearable Computers (ISWC) · doi:10.1145/3715071.3750404 · Cited by 1 · see citing works
Xin, Y., He, R., Ren, X., Yang, T., Wang, Y., & Su, X. (2025) · International Journal of COPD · doi:10.2147/copd.s566403 · Cited by 1 · see citing works
Applications of speech analysis in diseases’ assessment, prediction and diagnosis: a scoping review
Xu, X., Zhang, Y., Niu, Q., Long, N., Li, J., Zhao, L., Yin, J., & Yang, J. (2026) · Artificial Intelligence Surgery · doi:10.20517/ais.2025.70 · Cited by 2 · see citing works
A Comparative Analysis of Machine Learning Classifiers for Audio-Based Disease Classification
Varun, B., Jonnalagadda, I., Vardhan, C. V., Srinivas, M., & Ullas, S. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724370 · Cited by 1 · see citing works
Emadi, M., Taheri, M. H., Keshavarz, K., & Masoompour, S. M. (2023) · Research Square · doi:10.21203/rs.3.rs-3109536/v1
Sistemas diagnósticos de exacerbación de la EPOC en la población anciana: presente y futuro
Gálvez‐Barrón, C., & Pérez‐López, C. (2023) · Open Respiratory Archives · doi:10.1016/j.opresp.2023.100291
Zhou, J., Huang, J., Wang, Q., Yan, J., Cao, H., Chen, E., Ruan, X.-L., Zhu, W., Mao, J.-X., Wang, Q., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5025905
Xin, Y., He, R., Ren, X., Yang, T., Wang, Y., & Su, X. (2025) · Research Square · doi:10.21203/rs.3.rs-6437931/v1
Gong, Y., Xu, C., Mo, C., Wu, J., Lin, H., Su, H., Zhang, Q., Zhang, Q., & Yang, S. (2025) · medRxiv · doi:10.1101/2025.06.28.25330256
Gonzalez-Moral, S. G., Addis, P., Sharma, O., Oliver, A., Johnson, E. E., Al-Assaf, A. S., Sadiq, A., & Meader, N. (2025) · BMJ Innovations · doi:10.1136/bmjinnov-2024-001330
Sarkar, S., Datta, P., Datta, S., & Bhattacherjee, S. (2026) · The Indian Journal of Chest Diseases and Allied Sciences · doi:10.5005/jp-journals-11007-0189
Zhou, J., Huang, J., Wang, Q., Yan, J., Cao, H., Huang, L., Chen, S., Ruan, X., Zhu, W., Mao, J., et al. (2026) · npj Primary Care Respiratory Medicine · doi:10.1038/s41533-026-00486-6
- Authors
- Claxton, S., Porter, P., Brisbane, J., Bear, N., Wood, J., Peltonen, V., Della, P., Smith, C., & Abeyratne, U.
- Affiliations
- GenesisCare (AU)
- Curtin University (AU)
- The University of Notre Dame Australia (AU)
- The University of Queensland (AU)
- Corresponding author
- Porter, Paul
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2021-07-02
- Volume
- 4
- Issue
- 1
- Pages
- 107-107
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC8253790
- OpenAlex
- W3175291108
- Cited by
- 34
- References
- 35
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 7 · 2023: 7 · 2022: 5 · 2021: 1
- FWCI
- 3.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- COPD exacerbations, chronic obstructive pulmonary disease, smartphone-based diagnostics, patient-reported symptoms, speech recognition, fever, primary care, early diagnosis, patient self-management
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