ArticleScientific reports2024
Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Randomized clinical trial of ventilator liberation with pressure support ventilation versus therapist-implement patient-specific weaning in prolonged weaning patients via tracheostomy.BMC pulmonary medicine · 2026Trial
- Article
- Clinical Predictors of Weaning Failure and Mortality in Individuals With COVID-19 Undergoing Invasive Mechanical Ventilation: A Retrospective Cohort Study.Pulmonary medicine · 2026Article
- Non-invasive hair metabolome analysis reveals GERD and thyroid dysfunction in pulmonary fibrosis patients.Metabolism open · 2025Article
- AI-Driven Prediction of Glasgow Coma Scale Outcomes in Anterior Communicating Artery Aneurysms.Journal of clinical medicine · 2025Article
- Article
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for early clinical interventions, prevention of secondary complications such as nosocomial infections, and effective triage of hospital resources. This study tested the hypothesis that an ensemble machine learning (ML) analysis of clinical data at time of intubation could identify patients at risk of PMV, using a COVID-19 dataset to classify patients into PMV (> 14 days) and non-PMV (≤ 14 days) groups. While several factors are known to cause PMV, including acid-base, weakness, and delirium, lesser-utilized but routinely measured parameters such as platelet count, glucose levels and fevers may also be relevant. Patient data from a single University Hospital were analyzed via the ML workflow to predict patients at risk of PMV and identify key clinical markers. Model performance was evaluated on a chronologically distinct cohort. The ML workflow identified patients at risk of PMV with AUROC
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Registered trials
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