ArticleMedical & biological engineering & computing2015
Detecting COPD exacerbations early using daily telemonitoring of symptoms and k-means clustering: a pilot study.
Article in Medical & biological engineering & computing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review.BMC medical informatics and decision making · 2025Pooled it
- Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis.Journal of medical Internet research · 2024Pooled it
- Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review.Chronic respiratory disease · 2016Pooled it
- Impact of Telemonitoring With Exacerbation Prediction Algorithm Versus Telemonitoring Alone on Hospitalizations and Health-Related Quality of Life in Patients With COPD.Respiratory care · 2025Trial
- At-home validation of a non-contact, radar-based breathing monitor for long-term care of patients with respiratory diseases: A proof-of-concept study.Pneumologie (Stuttgart, Germany) · 2025Article
- Assessing the Impact of New Technologies on Managing Chronic Respiratory Diseases.Journal of clinical medicine · 2024Review
- Rationale and design of healthy at home for COPD: an integrated remote patient monitoring and virtual pulmonary rehabilitation pilot study.Pilot and feasibility studies · 2024Article
- Early detection and prediction of acute exacerbation of chronic obstructive pulmonary disease.Chinese medical journal pulmonary and critical care medicine · 2023Review
- Early detection of COPD patients' symptoms with personal environmental sensors: a remote sensing framework using probabilistic latent component analysis with linear dynamic systems.Neural computing & applications · 2023Article
- Estimating influences of unemployment and underemployment on mental health during the COVID-19 pandemic: who suffers the most?Public health · 2021Article
- An 11-Item Measure of User- and Human-Centered Design for Personal Health Tools (UCD-11): Development and Validation.Journal of medical Internet research · 2021Article
- Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks.Medical & biological engineering & computing · 2021Article
- Treating COPD Patients with Inhaled Medications in the Era of COVID-19 and Beyond: Options and Rationales for Patients at Home.International journal of chronic obstructive pulmonary disease · 2021Review
- Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks.IEEE journal of biomedical and health informatics · 2020Article
- How stable is lung function in patients with stable chronic obstructive pulmonary disease when monitored using a telehealth system? A longitudinal and home-based study.BMC medical informatics and decision making · 2020Article
- Telemonitoring to Manage Chronic Obstructive Pulmonary Disease: Systematic Literature Review.JMIR medical informatics · 2019Review
- Continuous remote monitoring of COPD patients-justification and explanation of the requirements and a survey of the available technologies.Medical & biological engineering & computing · 2018Review
- Machine learning approaches to personalize early prediction of asthma exacerbations.Annals of the New York Academy of Sciences · 2017Article
- Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD.Sensors (Basel, Switzerland) · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COPD places an enormous burden on the healthcare systems and causes diminished health-related quality of life. The highest proportion of human and economic cost is associated with admissions for acute exacerbation of respiratory symptoms (AECOPD). Since prompt detection and treatment of exacerbations may improve outcomes, early detection of AECOPD is a critical issue. This pilot study was aimed to determine whether a mobile health system could enable early detection of AECOPD on a day-to-day basis. A novel electronic questionnaire for the early detection of COPD exacerbations was evaluated during a 6-months field trial in a group of 16 patients. Pattern recognition techniques were applied. A k-means clustering algorithm was trained and validated, and its accuracy in detecting AECOPD was assessed. Sensitivity and specificity were 74.6 and 89.7 %, respectively, and area under the receiver operating characteristic curve was 0.84. 31 out of 33 AECOPD were early identified with an average of 4.5 ± 2.1 days prior to the onset of the exacerbation that was considered the day of medical attendance. Based on the findings of this preliminary pilot study, the proposed electronic questionnaire and the applied methodology could help to early detect COPD exacerbations on a day-to-day basis and therefore could provide support to patients and physicians.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.