ArticleScientific reports2020
A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 37 papers, 2 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it, 74 citations in OpenAlex.
- AI/ML driven prediction of COPD exacerbations and readmissions: a systematic review and meta-analysis.Frontiers in digital health · 2025Pooled it
- Prognostic risk prediction model for patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD): a systematic review and meta-analysis.Respiratory research · 2024Pooled it
- Respiratory sound-based AI screening of asthma and COPD via multi-feature fusion and CatBoost classification.Scientific reports · 2026Article
- 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
- Explainable machine learning model for 1-year readmission risk prediction in AECOPD patients: integrating relief feature selection with sample augmentation.BMC infectious diseases · 2025Article
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- A method for instrumental seismic intensity assessment in Western China based on RF and MLP.Scientific reports · 2025Article
- Development of an interpretable machine learning model for predicting prolonged hospital stay in patients with acute exacerbation of chronic obstructive pulmonary disease: a retrospective cohort study.BMC medical informatics and decision making · 2025Article
- Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease.Scientific reports · 2025Observational
- Machine learning approach to flare-up detection and clustering in chronic obstructive pulmonary disease (COPD) patients.Health information science and systems · 2024Article
- Development and Validation of Machine Learning-Based Models for Prediction of Intensive Care Unit Admission and In-Hospital Mortality in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2024Article
- Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer.Diagnostics (Basel, Switzerland) · 2024Article
- G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma.BMC cancer · 2024Article
- Integrating Artificial Intelligence in the Diagnosis of COPD Globally: A Way Forward.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2024Article
- Predicting early-onset COPD risk in adults aged 20-50 using electronic health records and machine learning.PeerJ · 2024Article
- Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19.Brain sciences · 2023Article
- Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population.Risk management and healthcare policy · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients with chronic obstructive pulmonary disease (COPD) repeat acute exacerbations (AE). Global Initiative for Chronic Obstructive Lung Disease (GOLD) is only available for patients in stable phase. Currently, there is a lack of assessment and prediction methods for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients during hospitalization. To enhance the monitoring and treatment of AECOPD patients, we develop a novel C5.0 decision tree classifier to predict the prognosis of AECOPD hospitalized patients with objective clinical indicators. The medical records of 410 hospitalized AECOPD patients are collected and 28 features including vital signs, medical history, comorbidities and various inflammatory indicators are selected. The overall accuracy of the proposed C5.0 decision tree classifier is 80.3% (65 out of 81 participants) with 95% Confidence Interval (CI):(0.6991, 0.8827) and Kappa 0.6054. In addition, the performance of the model constructed by C5.0 exceeds the C4.5, classification and regression tree (CART) model and the iterative dichotomiser 3 (ID3) model. The C5.0 decision tree classifier helps respiratory physicians to assess the severity of the patient early, thereby guiding the treatment strategy and improving the prognosis of patients.
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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.