VIF values for random forest prediction models.

<div><p>Objective</p><p>This study aimed to develop an innovative early prediction model for acute kidney injury (AKI) following cardiac surgery in intensive care unit (ICU) settings, leveraging preoperative and postoperative clinical variables, and to identify key risk facto...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Yang Xu (178421) (author)
مؤلفون آخرون:	Chunxiao Song (7026179) (author), Wenping Wei (6278807) (author), Runfeng Miao (21533316) (author)
منشور في:	2025
الموضوعات:	Cell Biology Genetics Biotechnology Developmental Biology Marine Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified youden &# 8217 performance via auroc model &# 8217 intensive care unit highest predictive accuracy enabling proactive interventions decision curve analysis critical physiological parameters clinically applicable tool analyzed three datasets acute kidney injury 7185 &# 8211 using logistic regression acute postoperative changes 7912 ), outperforming hour maximum values early aki prediction 028 aki cases postoperative clinical variables following cardiac surgery postoperative variables cardiac surgery postoperative 24 lasso regression dca ), hour maxima hour averages aki controls xlink "> superiority lies study highlights study aimed results offer random forest preoperative 48 leveraging preoperative iv database improve outcomes findings demonstrate dynamic preoperative baseline risks 276 non
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VIF values for random forest prediction models.

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