ArticleJournal of personalized medicine2022
A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The trial behind it
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Who cites it
12 citing papers in PubMed, 19 citations in OpenAlex.
- Artificial intelligence in mechanical ventilation: a narrative review of clinical applications and research gaps.Journal of thoracic disease · 2026Review
- AI Model Based on Diaphragm Ultrasound to Improve the Predictive Performance of Invasive Mechanical Ventilation Weaning: Prospective Cohort Study.JMIR formative research · 2025Article
- Role of artificial intelligence in enhancing mechanical ventilation - A peek into the future.Indian journal of anaesthesia · 2025Article
- Development and validation of machine learning models for predicting extubation failure in patients undergoing cardiac surgery: a retrospective study.Scientific reports · 2025Article
- The road toward a physiological control of artificial respiration: the role of bio-inspired neuronal networks.Frontiers in neuroscience · 2025Article
- Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.PloS one · 2025Article
- 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
- 5-methyladenosine regulators play a crucial role in development of chronic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis.Scientific reports · 2023Article
- Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review.Frontiers in medicine · 2023Article
- Relevance of RNA N6-Methyladenosine Regulators for Pulmonary Fibrosis: Implications for Chronic Hypersensitivity Pneumonitis and Idiopathic Pulmonary Fibrosis.Frontiers in genetics · 2022Article
- An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundVentilator weaning is one of the most significant challenges in the intensive care unit (ICU). Approximately 30% of patients fail to wean, resulting in prolonged use of ventilators and increased mortality. There are numerous high-performance prediction models available today, but they require a large number of parameters to predict and are thus impractical in clinical practice.
objectivesThis study aims to create an artificial intelligence (AI) model for predicting weaning time and to identify the most simplified key predictors that will allow the model to achieve adequate accuracy with as few parameters as possible.
methodsThis is a retrospective study of to-be-weaned patients (
resultsThe SVM [receiver operating characteristic curve (ROC-AUC) = 88%], logistic regression (ROC-AUC = 86%), and XGBoost (ROC-AUC = 85%) models outperformed the other five machine learning models in predicting weaning time. The accuracies in predicting patient weaning within 24 h using seven variables (i.e., expiratory minute ventilation, expiratory tidal volume, ventilation rate set, heart rate, peak pressure, pH, and age) were close to those using 28 variables.
conclusionsThe model developed in this research successfully predicted the weaning success of ICU patients using a few and easily accessible parameters such as age. Therefore, it can be used in clinical practice to identify difficult-to-wean patients to improve their treatment.
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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 OpenQuestion graph.