Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making

Shivi Mendiratta; Vinay Gandhi Mukkelli; Esha Baidya Kayal; Puneet Khanna; Amit Mehndiratta

doi:10.1177/20552076251352988

May 1, 2025SHILAP Revista de lepidopterologíaOpen Access

Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making

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Key Points

Develop an interpretable machine learning framework to predict the duration of mechanical ventilation for patients in intensive care units.
Designed a data-driven machine learning model to estimate mechanical ventilation duration in ICU settings.
Applied SHapley Additive exPlanations (SHAP)-based feature selection to improve model transparency and interpretability.
Machine learning models effectively predict ventilation timelines to support clinical decision-making, optimize resource allocation, and enable personalized care.
SHAP-based feature selection provides interpretable insights into model predictions, facilitating clinical trust and adoption.

Implication

Machine learning study demonstrates ventilation duration prediction in intensive care units, suggesting improved resource optimization.

Abstract

AI-driven prediction of ventilation duration can enhance ICU workflows, optimize resource use, and improve personalized care. SHAP-based feature selection promotes AI interpretability, aiding clinical adoption.

Discussion

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Cite This Study

Mendiratta et al. (2025) studied this question.

synapsesocial.com/papers/69dac7bd0d8d6ef495a3c237 https://doi.org/10.1177/20552076251352988

View Full Paper

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Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making

Key Points

Implication

Abstract

Discussion

Cite This Study

Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making

Key Points

Implication

Abstract

Discussion

Cite This Study

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