ArticleFrontiers in medicine2021
A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases.
Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.BMC infectious diseases · 2023Pooled it
- Trial
- Cross-Silo Federated Learning for Predicting Successful Mechanical Ventilation Weaning Across 5 Intensive Care Unit Databases: Retrospective Database Analysis.JMIR medical informatics · 2026Article
- AI-Driven Predictions of Readmission and Mortality for Improved Discharge Decisions in Critical Care: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2026Article
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- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Fine tuned CatBoost machine learning approach for early detection of cardiovascular disease through predictive modeling.Scientific reports · 2025Article
- Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.Internal and emergency medicine · 2025Article
- Using weak signals to predict spontaneous breathing trial success: a machine learning approach.Intensive care medicine experimental · 2025Article
- Development of an interpretable machine learning model for predicting venous thromboembolism in intensive care unit patients with intracerebral hemorrhage.Frontiers in neurology · 2025Article
- The role of Open Access Data in democratizing healthcare AI: A pathway to research enhancement, patient well-being and treatment equity in Andalusia, Spain.PLOS digital health · 2024Article
- Article
- Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical Diagnosis Data from the MIMIC-IV Database.Bioengineering (Basel, Switzerland) · 2024Article
- Predicting Successful Weaning from Mechanical Ventilation by Reduction in Positive End-expiratory Pressure Level Using Machine Learning.PLOS digital health · 2024Article
- Explainable Machine Learning to Predict Successful Weaning of Mechanical Ventilation in Critically Ill Patients Requiring Hemodialysis.Healthcare (Basel, Switzerland) · 2023Article
- Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation.Frontiers in neurology · 2023Article
- Explainable machine learning approach to predict extubation in critically ill ventilated patients: a retrospective study in central Taiwan.BMC anesthesiology · 2022Article
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9 authors.
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Abstract
backgroundInvasive mechanical ventilation plays an important role in the prognosis of patients with sepsis. However, there are, currently, no tools specifically designed to assess weaning from invasive mechanical ventilation in patients with sepsis. The aim of our study was to develop a practical model to predict weaning in patients with sepsis.
methodsWe extracted patient information from the Medical Information Mart for Intensive Care Database-IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Kaplan-Meier curves were plotted to compare the 28-day mortality between patients who successfully weaned and those who failed to wean. Subsequently, MIMIC-IV was divided into a training set and an internal verification set, and the eICU-CRD was designated as the external verification set. We selected the best model to simplify the internal and external validation sets based on the performance of the model.
resultsA total of 5020 and 7081 sepsis patients with invasive mechanical ventilation in MIMIC-IV and eICU-CRD were included, respectively. After matching, weaning was independently associated with 28-day mortality and length of ICU stay (
conclusionsWeaning success is independently related to short-term mortality in patients with sepsis. The simplified model based on the XGBoost algorithm provides good predictive performance and great clinical applicablity for weaning, and a web-based tool was developed for better clinical application.
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