ArticleDiagnostics (Basel, Switzerland)2022
Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 35 citations in OpenAlex.
- Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.BMC endocrine disorders · 2023Trial
- Cross-Silo Federated Learning for Predicting Successful Mechanical Ventilation Weaning Across 5 Intensive Care Unit Databases: Retrospective Database Analysis.JMIR medical informatics · 2026Article
- [Research progress in artificial intelligence for the diagnosis and management of diseases in preterm infants].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- The rise of artificial intelligence in respiratory primary care and pulmonology: a scoping review.NPJ primary care respiratory medicine · 2026Article
- Pulmonary Cryptococcosis Infection in Non-HIV Patients in a Tertiary Care Center in Taiwan.Pulmonary medicine · 2026Review
- Machine learning for prediction of weaning and extubation from mechanical ventilation: a systematic review of methodology, reporting and bias.BMJ digital health & AI · 2026Article
- Mechanical power normalized to dynamic lung compliance as a predictor of weaning and prognosis in mechanically ventilated patients: a retrospective cohort study.BMC anesthesiology · 2025Observational
- AI Model Based on Diaphragm Ultrasound to Improve the Predictive Performance of Invasive Mechanical Ventilation Weaning: Prospective Cohort Study.JMIR formative research · 2025Article
- Artificial intelligence-driven decision support for patients with acute respiratory failure: a scoping review.Intensive care medicine experimental · 2025Review
- Review
- Role of artificial intelligence in enhancing mechanical ventilation - A peek into the future.Indian journal of anaesthesia · 2025Article
- Artificial intelligence and machine learning in acute respiratory distress syndrome management: recent advances.Frontiers in medicine · 2025Review
- 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
- Artificial Intelligence in the Management of Patients with Respiratory Failure Requiring Mechanical Ventilation: A Scoping Review.Journal of clinical medicine · 2024Article
- Acute Respiratory Failure in Children: A Clinical Update on Diagnosis.Children (Basel, Switzerland) · 2024Review
- Machine learning-based risk prediction model construction of difficult weaning in ICU patients with mechanical ventilation.Scientific reports · 2024Article
- Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data.Sensors (Basel, Switzerland) · 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
- Predicting Successful Weaning from Mechanical Ventilation by Reduction in Positive End-expiratory Pressure Level Using Machine Learning.PLOS digital health · 2024Article
- Artificial intelligence in respiratory care.Frontiers in digital health · 2024Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Successful weaning from prolonged mechanical ventilation (MV) is an important issue in respiratory care centers (RCCs). Delayed or premature extubation increases both the risk of adverse outcomes and healthcare costs. However, the accurate evaluation of the timing of successful weaning from MV is very challenging in RCCs. This study aims to utilize artificial intelligence algorithms to build predictive models for the successful timing of the weaning of patients from MV in RCCs and to implement a dashboard with the best model in RCC settings. A total of 670 intubated patients in the RCC in Chi Mei Medical Center were included in the study. Twenty-six feature variables were selected to build the predictive models with artificial intelligence (AI)/machine-learning (ML) algorithms. An interactive dashboard with the best model was developed and deployed. A preliminary impact analysis was then conducted. Our results showed that all seven predictive models had a high area under the receiver operating characteristic curve (AUC), which ranged from 0.792 to 0.868. The preliminary impact analysis revealed that the mean number of ventilator days required for the successful weaning of the patients was reduced by 0.5 after AI intervention. The development of an AI prediction dashboard is a promising method to assist in the prediction of the optimal timing of weaning from MV in RCC settings. However, a systematic prospective study of AI intervention is still needed.
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