ArticleiScience2024
Machine learning approaches for practical predicting outpatient near-future AECOPD based on nationwide electronic medical records.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial Intelligence for Early Detection and Prediction of Chronic Obstructive Pulmonary Disease Exacerbations.Healthcare (Basel, Switzerland) · 2026Review
- Exploring the impact of AI technostress on physicians' job insecurity and performance from an empirical multi-hospital study.iScience · 2026Article
- An interpretable ensemble learning framework for COPD classification using population-based clinical and laboratory data from NHANES.Tobacco induced diseases · 2026Article
- Multimodal machine learning predicts type 2 respiratory failure in COPD exacerbations: a multicenter XGBoost model with clinical nomogram.Frontiers in medicine · 2026Article
- An interpretable stacked ensemble framework for evaluating respiratory rehabilitation outcomes under traditional Chinese medicine-integrated care: a multicenter retrospective cohort study.Frontiers in medicine · 2026Article
- An Interpretable AdaBoost Model for 1-Year Readmission Risk Prediction in AECOPD Patients with Hypertension.International journal of chronic obstructive pulmonary disease · 2026Article
- Pulmonary Emphysema: Current Understanding of Disease Pathogenesis and Therapeutic Approaches.Biomedicines · 2025Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this research, we aimed to harness machine learning to predict the imminent risk of acute exacerbation in chronic obstructive pulmonary disease (AECOPD) patients. Utilizing retrospective data from electronic medical records of two Taiwanese hospitals, we identified 26 critical features. To predict 3- and 6-month AECOPD occurrences, we deployed five distinct machine learning algorithms alongside ensemble learning. The 3-month risk prediction was best realized by the XGBoost model, achieving an AUC of 0.795, whereas the XGBoost was superior for the 6-month prediction with an AUC of 0.813. We conducted an explainability analysis and found that the episode of AECOPD, mMRC score, CAT score, respiratory rate, and the use of inhaled corticosteroids were the most impactful features. Notably, our approach surpassed predictions that relied solely on CAT or mMRC scores. Accordingly, we designed an interactive prediction system that provides physicians with a practical tool to predict near-term AECOPD risk in outpatients.
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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 Socratic graph.