ArticleScientific reports2020
Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment-implications for clinical practice.Frontiers in medical technology · 2025Pooled it
- Development of an Artificial Intelligence Model to Predict Endotracheal Intubation in Critically Ill Patients in Real Time.Journal of clinical medicine · 2026Article
- Artificial intelligence algorithm to predict the requirement of neonatal endotracheal intubation within 3 h: application for clinical practice.Frontiers in medicine · 2026Article
- RMS: a ML-based system for ICU respiratory monitoring and resource planning.NPJ digital medicine · 2025Article
- Development and validation of machine learning models for distal instrumentation-related problems in patients with degenerative lumbar scoliosis based on preoperative CT and MRI.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 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
- Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases.Medical & biological engineering & computing · 2024Article
- Mortality Prediction of Biochemical Parameters in Patients Intubated in the Emergency Department.Cureus · 2024Article
- Effect of timing of intubation on clinical outcomes of patients with septic shock: a retrospective cohort study.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
- A Reliable Method for Quantifying Plasma Cell-Free DNA Using an Internal Standard Strategy: Evaluation in a Cohort of Non-Pregnant and Pregnant Women.Reproductive sciences (Thousand Oaks, Calif.) · 2024Article
- 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
- Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network.Scientific reports · 2023Article
- The NUTRIC Score as a Tool to Predict Mortality and Increased Resource Utilization in Intensive Care Patients with Sepsis.Nutrients · 2023Article
- Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- Machine learning for the prediction of preclinical airway management in injured patients: a registry-based trial.Clinical and experimental emergency medicine · 2022Article
- Data science in the intensive care unit.World journal of critical care medicine · 2022Article
- A multi-task Gaussian process self-attention neural network for real-time prediction of the need for mechanical ventilators in COVID-19 patients.Journal of biomedical informatics · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding high risk late intubation. This study evaluates whether machine learning can predict the need for intubation within 24 h using commonly available bedside and laboratory parameters taken at critical care admission. We extracted data from 2 large critical care databases (MIMIC-III and eICU-CRD). Missing variables were imputed using autoencoder. Machine learning classifiers using logistic regression and random forest were trained using 60% of the data and tested using the remaining 40% of the data. We compared the performance of logistic regression and random forest models to predict intubation in critically ill patients. After excluding patients with limitations of therapy and missing data, we included 17,616 critically ill patients in this retrospective cohort. Within 24 h of admission, 2,292 patients required intubation, whilst 15,324 patients were not intubated. Blood gas parameters (P
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