ArticleJournal of critical care2021
Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19.
Article in Journal of critical care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Optimizing Intubation Prediction in Pneumonia Patients: A Systematic Review and Meta-Analysis of Machine Learning Algorithms.Pulmonary medicine · 2026Pooled it
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Machine learning-based risk prediction of outcomes in patients hospitalized with COVID-19 in Australia: the AUS-COVID Score.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- AI-based decision models for difficult airway assessment: from research innovation to clinical implementation-a narrative review.Frontiers in medicine · 2026Review
- Artificial intelligence algorithm to predict the requirement of neonatal endotracheal intubation within 3 h: application for clinical practice.Frontiers in medicine · 2026Article
- Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.PloS one · 2025Article
- Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters.BMC anesthesiology · 2024Article
- Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.Scientific reports · 2024Article
- Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.Scientific reports · 2024Article
- A risk prediction model for efficient intubation in the emergency department: A 4-year single-center retrospective analysis.Journal of the American College of Emergency Physicians open · 2024Article
- COVID-19 outbreaks surveillance through text mining applied to electronic health records.BMC infectious diseases · 2024Article
- Construction and optimization of vending machine decision support system based on improved C4.5 decision tree.Heliyon · 2024Article
- Article
- Prognostic analysis of high-flow nasal cannula therapy and non-invasive ventilation in mild to moderate hypoxemia patients and construction of a machine learning model for 48-h intubation prediction-a retrospective analysis of the MIMIC database.Frontiers in medicine · 2024Article
- Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction.Scientific reports · 2023Article
- A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.JMIR formative research · 2023Article
- Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.Scientific reports · 2023Article
- Predicting the mortality of patients with Covid-19: A machine learning approach.Health science reports · 2023Article
- Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks.Journal of education and health promotion · 2023Article
- Coronavirus disease (COVID-19) cases analysis using machine-learning applications.Applied nanoscience · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeThe purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19. MATERIALS AND
methodsThis is a retrospective cohort study of patients diagnosed or under investigation for COVID-19. A machine learning algorithm was trained to predict future presence of intubation based on prior vitals, laboratory, and demographic data. Model performance was compared to ROX index, a validated prognostic tool for prediction of mechanical ventilation.
results4087 patients admitted to five hospitals between February 2020 and April 2020 were included. 11.03% of patients were intubated. The machine learning model outperformed the ROX-index, demonstrating an area under the receiver characteristic curve (AUC) of 0.84 and 0.64, and area under the precision-recall curve (AUPRC) of 0.30 and 0.13, respectively. In the Kaplan-Meier analysis, patients alerted by the model were more likely to require intubation during their admission (p < 0.0001).
conclusionIn patients diagnosed or under investigation for COVID-19, machine learning can be used to predict future risk of intubation based on clinical data which are routinely collected and available in clinical setting. Such an approach may facilitate identification of high-risk patients to assist in clinical care.
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