Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU

The optimized predictive model, which was developed through the integration of multidimensional predictive factors with diverse machine learning algorithms, exhibits superior predictive accuracy and demonstrates significant clinical potential for determining the optimal timing for weaning patients receiving invasive mechanical ventilation. Trial registration Current Controlled Trials ChiCTR2400093658; registration date: December 10, 2024.

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Publication Details

Journal
Intensive Care Medicine Experimental
Published
2026-01-21
DOI
https://doi.org/10.1186/s40635-026-00859-8
Primary Topic
Respiratory Support and Mechanisms
Type
article
Field-Weighted Citation Impact
0.00

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article

Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU

Lulu Tang, YuGang Zhuang, Kevin Zheng, Changcui Qiu et al.
Intensive Care Medicine Experimental
Respiratory Support and Mechanisms
article

Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU

Lulu Tang, YuGang Zhuang, Kevin Zheng, Changcui Qiu, Chunwei Chi, Xiaoping Zhu
article en

Abstract

The optimized predictive model, which was developed through the integration of multidimensional predictive factors with diverse machine learning algorithms, exhibits superior predictive accuracy and demonstrates significant clinical potential for determining the optimal timing for weaning patients receiving invasive mechanical ventilation. Trial registration Current Controlled Trials ChiCTR2400093658; registration date: December 10, 2024.

Intensive Care Medicine ExperimentalVol. 14(1)
Tongji University (CN), Shanghai Tenth People's Hospital (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 84%
Respiratory Support and Mechanisms
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