October 2, 2025

Machine Learning Models for Predicting 28-Day Mortality in Gastrointestinal Bleeding with Acute Kidney Injury: A MIMIC-IV-Based Study

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XGBoost model achieved an area under the curve of 0.8027 for predicting 28-day mortality in patients with GIB-AKI.
Five machine learning algorithms were developed and compared, enhancing the interpretability of mortality predictors using SHAP.
The study utilized the MIMIC-IV database including 1,890 adult patients to build and validate these prediction models.
Results highlight the potential of machine learning to improve early mortality prediction and critical care management for GIB-AKI patients.

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Implication

Retrospective cohort study identified key predictors of mortality in GIB-AKI patients, suggesting machine learning models enhance clinical decision-making.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68de68e583cbc991d0a21233 https://doi.org/10.21203/rs.3.rs-7339661/v1

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Implication

Retrospective cohort study identified key predictors of mortality in GIB-AKI patients, suggesting machine learning models enhance clinical decision-making.

Machine Learning Models for Predicting 28-Day Mortality in Gastrointestinal Bleeding with Acute Kidney Injury: A MIMIC-IV-Based Study

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Discussion

Implication

Cite This Study

Machine Learning Models for Predicting 28-Day Mortality in Gastrointestinal Bleeding with Acute Kidney Injury: A MIMIC-IV-Based Study

Key Points

Discussion

Implication

Cite This Study

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