Prediction performance of each model.

<div><p>Background</p><p>Mechanical ventilation, a crucial intervention for acute respiratory distress syndrome (ARDS), can lead to ventilator-induced lung injury (VILI). This study focuses on individualizing mechanical power (MP) in mechanically ventilated patients to minimi...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Ahmed S. Alkhalifah (20721713) (author)
مؤلفون آخرون:	Kenny Rumindo (20721716) (author), Edgar Brincat (20721719) (author), Florian Blanchard (12793247) (author), Johan Helleberg (11092280) (author), David Clarke (79349) (author), Benjamin Popoff (8863031) (author), Olivier Duranteau (20721722) (author), Zubair Umer Mohamed (20721725) (author), Abdelrahman Senosy (20721728) (author)
منشور في:	2025
الموضوعات:	Biophysics Medicine Cell Biology Biotechnology Developmental Biology Cancer Infectious Diseases xgboost model showed time physiological variables significantly higher time safe upper limits safe upper limit machine learning models least 48 hours induced lung injury including logistic regression ideal body weight highest predictive accuracy optimizing mechanical ventilation mechanical ventilation settings controlled mechanical ventilation personalizing mechanical power individualizing mechanical power study &# 8217 predict icu mortality increased icu mortality xlink "> despite weighted average mp mechanically ventilated patients hypoxemia groups based mechanical power icu mortality xlink "> study highlights study focuses surrogate formula statistical analyses retrospective design retrospective analysis random forest multifactorial nature individualization method fio2 ratio driven approach crucial intervention conducted using calculated using ards ), 338 met 106 admissions
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Prediction performance of each model.

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