ArticleScientific reports2025
Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Deep Learning-Based Parkinson's Disease Classification Using RGB Plantar Pressure Gait Images: A Comparative Study of CNN and Transformer Architectures.Diagnostics (Basel, Switzerland) · 2026Article
- Optimized explainable AI and digital twin for patient flow improvement in ICU during respiratory epidemics.BMC medical informatics and decision making · 2026Article
- Optimize a chain convenience store location prediction model by using MTS-machine learning methodology.Scientific reports · 2025Article
- Factors Influencing Stroke Severity Based on Collateral Circulation, Clinical Markers and Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- A comprehensive study based on machine learning models for early identification Mycoplasma pneumoniae infection in segmental/lobar pneumonia.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The advancement of the Internet of Medical Things (IoMT) has revolutionized data acquisition and processing in critical care settings. Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient care. This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators. The system was deployed on edge devices to enable on-site inference with minimal latency. Among the evaluated models, Random Forest and XGBoost, the latter demonstrated superior predictive performance under both holdout and tenfold cross-validation schemes. Notably, the edge-based architecture reduced server data transmissions by 83.33%, while improving system stability, resilience, and sustainability. This paper details the model evaluation and demonstrates the feasibility and efficiency of edge intelligence in ventilator weaning decision support.
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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.