ArticleBioengineering (Basel, Switzerland)2023
Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07733102 (Regional Diaphragmatic Synchrony Index for Predicting Weaning Outcomes During Pressure Support Ventilation Spontaneous Breathing Trials), which is not on this map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Regional Diaphragmatic Synchrony Index for Predicting Weaning Outcomes During Pressure Support Ventilation Spontaneous Breathing Trials: A Prospective Observational Study
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Nurse-led weaning protocols-a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Phenotype-based modeling of postoperative ventilatory liberation failure: A dynamic mechanistic framework for precision perioperative medicine.Journal of anesthesia and translational medicine · 2026Review
- MetaboSense: An Integrated Quantum Dot-Mediated Aptamer Assay and Modular Microfluidic Platform for Continuous In-Line Monitoring of Lung Metabolism During Ex Vivo Perfusion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Liberation from Mechanical Ventilation in Acute Hypoxemic Respiratory Failure or Adult Respiratory Distress Syndrome: A Review.Journal of clinical medicine · 2026Review
- Artificial intelligence in ARDS: From automated support to personalized ventilation.Journal of intensive medicine · 2026Article
- Special Issue: "Artificial Intelligence for Biomedical Signal Processing".Bioengineering (Basel, Switzerland) · 2025Article
- Using ML techniques to predict extubation outcomes for patients with central nervous system injuries in the Yun-Gui Plateau.Scientific reports · 2025Article
- Using weak signals to predict spontaneous breathing trial success: a machine learning approach.Intensive care medicine experimental · 2025Article
- A Nomogram of Weaning Failure for Critical Ventilated Patients in High-Altitude Areas: A Single-Center Cohort Study Using Lasso Logistic Regression.Anesthesiology research and practice · 2025Article
- Machine learning to predict extubation success using the spontaneous breathing trial, objective cough measurement, and diaphragmatic contraction velocity: Secondary analysis of the COBRE-US trial.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2025Article
- Advances in Machine Learning for Mechanically Ventilated Patients.International journal of general medicine · 2025Review
- Predictors of weaning failure in ventilated intensive care patients: a systematic evidence map.Critical care (London, England) · 2024Review
- Blood gas analysis: Clinical applications, interpretation and future directions (Review).Medicine internationalReview
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019-2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795-1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434-0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model's prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes.
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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.