ArticleScientific reports2023
Prediction prolonged mechanical ventilation in trauma patients of the intensive care unit according to initial medical factors: a machine learning approach.
Article in Scientific reports, 2023. 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 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Artificial intelligence in mechanical ventilation: a narrative review of clinical applications and research gaps.Journal of thoracic disease · 2026Review
- An Artificial Intelligence Approach to Predict Tracheostomy Requirement in Mechanically Ventilated Critically Ill Patients: A Retrospective Single-Center Study.Journal of clinical medicine · 2026Article
- Development and external validation of a nomogram to predict prolonged postoperative mechanical ventilation in patients with acute type A aortic dissection.Frontiers in cardiovascular medicine · 2026Article
- Risk factors for prolonged mechanical ventilation in critically ill patients with ischemic stroke.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Article
- Article
- Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.PloS one · 2025Article
- Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.Scientific reports · 2024Article
- Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study.Journal of clinical medicine · 2024Article
- Machine learning prediction of the total duration of invasive and non-invasive ventilation During ICU Stay.PLOS digital health · 2023Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The goal of this study was to develop a predictive machine learning model to predict the risk of prolonged mechanical ventilation (PMV) in patients admitted to the intensive care unit (ICU), with a focus on laboratory and Arterial Blood Gas (ABG) data. This retrospective cohort study included ICU patients admitted to Rajaei Hospital in Shiraz between 2016 and March 20, 2022. All adult patients requiring mechanical ventilation and seeking ICU admission had their data analyzed. Six models were created in this study using five machine learning models (PMV more than 3, 5, 7, 10, 14, and 23 days). Patients' demographic characteristics, Apache II, laboratory information, ABG, and comorbidity were predictors. This study used Logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and C.5 decision tree (C.5 DT) to predict PMV. The study enrolled 1138 eligible patients, excluding brain-dead patients and those without mechanical ventilation or a tracheostomy. The model PMV > 14 days showed the best performance (Accuracy: 83.63-98.54). The essential ABG variables in our two optimal models (artificial neural network and decision tree) in the PMV > 14 models include FiO
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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.