ArticlePloS one2017
A machine learning approach to triaging patients with chronic obstructive pulmonary disease.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Development and Validation of an Electronic Health Record Algorithm to Predict the Presence of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2026Article
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
- Prediction of Work-relatedness of Shoulder Musculoskeletal Disorders as by Using Machine Learning.Safety and health at work · 2025Article
- Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.Journal of medical Internet research · 2025Article
- Development of a web application to evaluate spirometric curve and clinical variables to support COPD diagnosis in primary care.Biomedica : revista del Instituto Nacional de Salud · 2024Article
- Curve-Modelling and Machine Learning for a Better COPD Diagnosis.International journal of chronic obstructive pulmonary disease · 2024Observational
- Article
- Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk.PloS one · 2024Article
- Decoding Wilson disease: a machine learning approach to predict neurological symptoms.Frontiers in neurology · 2024Article
- The Challenges of Spirometric Diagnosis of COPD.Canadian respiratory journal · 2023Review
- Classification of Ear Imagery Database using Bayesian Optimization based on CNN-LSTM Architecture.Journal of digital imaging · 2022Article
- Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method.Computers in biology and medicine · 2022Article
- Machine Learning for Predicting Discharge Disposition After Traumatic Brain Injury.Neurosurgery · 2022Article
- Computer-Aided Detection of Respiratory Sounds in Bronchial Asthma Patients Based on Machine Learning Method.Sovremennye tekhnologii v meditsine · 2022Article
- Identification and Prediction of Chronic Diseases Using Machine Learning Approach.Journal of healthcare engineering · 2022Article
- Exploratory study on classification of chronic obstructive pulmonary disease combining multi-stage feature fusion and machine learning.BMC medical informatics and decision making · 2021Article
- Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
- Machine Learning and Syncope Management in the ED: The Future Is Coming.Medicina (Kaunas, Lithuania) · 2021Review
- Indirectly-Supervised Anomaly Detection of Clinically-Meaningful Health Events from Smart Home Data.ACM transactions on intelligent systems and technology · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacerbations and subsequent triage. Our application uses physician opinion in a statistically and clinically comprehensive set of patient cases to train a supervised prediction algorithm. The accuracy of the model is assessed against a panel of physicians each triaging identical cases in a representative patient validation set. Our results show that algorithm accuracy and safety indicators surpass all individual pulmonologists in both identifying exacerbations and predicting the consensus triage in a 101 case validation set. The algorithm is also the top performer in sensitivity, specificity, and ppv when predicting a patient's need for emergency care.
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Registered trials
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.