ArticleJournal of clinical medicine2018
An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units.
Article in Journal of clinical medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 3 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Risk prediction models for extubation failure in critically ill patients on mechanical ventilation: a systematic review.Frontiers in medicine · 2025Pooled it
- Pooled Analysis of Central Venous Pressure and Brain Natriuretic Peptide Levels in Patients With Extubation Failure.Frontiers in physiology · 2022Pooled it
- Prediction of extubation outcome in critically ill patients: a systematic review and meta-analysis.Critical care (London, England) · 2021Pooled it
- Liberation from Mechanical Ventilation in Acute Hypoxemic Respiratory Failure or Adult Respiratory Distress Syndrome: A Review.Journal of clinical medicine · 2026Review
- Feature selection methodology based on explainable AI: application in predicting extubation success in the intensive care unit.Scientific reports · 2026Article
- Predictive Analysis of Extubation Failure in the Paediatric Intensive Care Unit in Bloemfontein, South Africa.Reports (MDPI) · 2026Article
- Development of a deep-learning model for classification of intensivists' extubation decisions using ventilator graphic monitor images: a prospective observational study.Journal of intensive care · 2026Article
- Machine learning-based risk prediction of 28-day mortality for sepsis patients with augmented renal clearance.Scientific reports · 2026Article
- Predicting weaning failure from invasive mechanical ventilation: The promise and pitfalls of clinical prediction scores.World journal of critical care medicine · 2025Review
- Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.Scientific reports · 2025Article
- Development and validation of machine learning models for predicting extubation failure in patients undergoing cardiac surgery: a retrospective study.Scientific reports · 2025Article
- Advances in Machine Learning for Mechanically Ventilated Patients.International journal of general medicine · 2025Review
- Article
- Challenges in Transitioning from Controlled to Assisted Ventilation in Acute Respiratory Distress Syndrome (ARDS) Management.Journal of clinical medicine · 2024Article
- Predictors of weaning failure in ventilated intensive care patients: a systematic evidence map.Critical care (London, England) · 2024Review
- Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation.Bioengineering (Basel, Switzerland) · 2024Article
- Development and validation of a clinical prediction model for early ventilator weaning in post-cardiac surgery.Heliyon · 2024Article
- Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome.Journal of clinical medicine · 2024Review
- Defining predictors for successful mechanical ventilation weaning, using a data-mining process and artificial intelligence.Scientific reports · 2023Article
- Work of Breathing During Proportional Assist Ventilation as a Predictor of Extubation Failure.Respiratory care · 2023Observational
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSuccessful weaning from mechanical ventilation is important for patients in intensive care units (ICUs). The aim was to construct neural networks to predict successful extubation in ventilated patients in ICUs.
methodsData from 1/12/2009 through 31/12/2011 of 3602 patients with planned extubation in Chi-Mei Medical Center's ICUs was used to train and test an artificial neural network (ANN). The input was 37 clinical risk factors, and the output was a failed extubation prediction.
resultsOne hundred eighty-five patients (5.1%) had a failed extubation. Multivariate analyses revealed that failure was positively associated with therapeutic intervention scoring system (TISS) scores (odds ratio [OR]: 1.814; 95% Confidence Interval [CI]: 1.283⁻2.563), chronic hemodialysis (OR: 12.264; 95% CI: 8.556⁻17.580), rapid shallow breathing (RSI) (OR: 2.003; 95% CI: 1.378⁻2.910), and pre-extubation heart rate (OR: 1.705; 95% CI: 1.173⁻2.480), but negatively associated with pre-extubation PaO₂/FiO₂ (OR: 0.529; 95%: 0.370⁻0.750) and maximum expiratory pressure (MEP) (OR: 0.610; 95% CI: 0.413⁻0.899). A multilayer perceptron ANN model with 19 neurons in a hidden layer was developed. The overall performance of this model was F₁: 0.867, precision: 0.939, and recall: 0.822. The area under the receiver operating characteristic curve (AUC) was 0.85, which is better than any one of the following predictors: TISS: 0.58 (95% CI: 0.54⁻0.62;
conclusionsThe ANN performed well when predicting failed extubation, and it will help predict successful planned extubation.
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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.