An Ensemble Machine Learning Model for Early Prediction of Vancomycin-Induced Acute Kidney Injury in ICU Patients.

The findings suggest that integrating advanced machine learning techniques with interpretable artificial intelligence (AI) can provide a scalable and cost-effective solution for early AKI detection in diverse healthcare settings.

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Publication Details

Journal
PubMed
Published
2025-01-01
DOI
https://doi.org/10.22037/aaemj.v13i1.2560
Citations
1
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
1.35
Controls
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article

An Ensemble Machine Learning Model for Early Prediction of Vancomycin-Induced Acute Kidney Injury in ICU Patients.

Farzaneh Futuhi, Ahmad Ali Abin, Faezeh Aghamirzaei
1 citations
PubMed
Sepsis Diagnosis and Treatment
1.35
article

An Ensemble Machine Learning Model for Early Prediction of Vancomycin-Induced Acute Kidney Injury in ICU Patients.

Farzaneh Futuhi, Ahmad Ali Abin, Faezeh Aghamirzaei
article en
1 citations

Abstract

The findings suggest that integrating advanced machine learning techniques with interpretable artificial intelligence (AI) can provide a scalable and cost-effective solution for early AKI detection in diverse healthcare settings.

PubMedVol. 13(1)
Shahid Beheshti University (IR)
Openalex Percentile: Top 19%
Sepsis Diagnosis and Treatment
1.35
DOIOpen Access PDFOpenAlex
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An Ensemble Machine Learning Model for Early Prediction of Vancomycin-Induced Acute Kidney Injury in ICU Patients. — Farzaneh Futuhi, Ahmad Ali Abin, et al. · PubMed (2025) | TGRS Research Map | TGRS