Stage prediction of acute kidney injury in sepsis patients using explainable machine learning approaches - Original Research - Published 15 October 2025

Aug 4, 2026

Acute kidney injury (AKI) is a common, high-mortality complication in sepsis, making early risk forecasting essential for timely treatment and better outcomes. This study develops and evaluates machine learning frameworks to predict AKI progression in high-risk sepsis patients, emphasizing explainability for clinical translation. Using the MIMIC-IV critical care dataset with feature selection, multiple algorithms are trained and validated via five-fold cross testing. SHAP is applied to interpret influential factors and support personalized intervention strategies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Sepsis-associated AKI and the need for early identification

Limitations of binary prediction and rationale for multi-class staging (KDIGO)

Methods

Dataset and cohort extraction (MIMIC-IV)

Feature selection and model training

Five-fold cross testing and performance assessment

Explainability using SHAP

OPEN ACCESS EDITED BY Nozomi Takahashi, University of British Columbia, Canada REVIEWED BY Harikrishna Choudary Ponnam, Summa Health System, United States Parviz Ghafariasl, Kansas State University Olathe, United States *CORRESPONDENCE Hongliang Wang [icuwanghongliang@hrbmu.edu.cn](icuwanghongliang@hrbmu.edu.cn) RECEIVED 16 July 2025 ACCEPTED 01 October 2025 PUBLISHED 15 October 2025 CITATION Quan Z, Han Z, Zeng S, Wen L, Wang J, Li Y and Wang H (2025) Stage prediction of acute kidney injury in sepsis patients using explainable machine learning approaches. Front. Med. 12:1667488 . doi: 10.3389/fmed.2025.1667488 COPYRIGHT © 2025 Quan, Han, Zeng, Wen, Wang, Li and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Original Research PUBLISHED 15 October 2025 DOI 10.3389/fmed.2025.1667488 Stage prediction of acute kidney injury in sepsis patients using explainable machine learning approaches Zhen Quan 1, Zheng Han 1, Siyao Zeng 1, Lianghe Wen 2, Jingkai Wang 1, Yue Li 2 and Hongliang Wang 2* 1The Second Clinical Medical College of Harbin Medical University, Harbin, Heilongjiang Province, China, 2 Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China Background: Acute kidney injury (AKI) is a prevalent and serious complication among sepsis patients, closely associated with high mortality rates and substantial disease burden. Early prediction of AKI is vital for prompt and effective intervention and improved prognosis. This research seeks to construct and assess forecasting frameworks that leverage advanced machine learning algorithms to anticipate AKI progression in high-risk sepsis patients. Methods: This study utilized the MIMIC-IV database, a large, publicly available critical care dataset containing comprehensive, de-identified electronic health records of over 70,000 ICU admissions at Beth Israel Deaconess Medical Center, to extract sepsis patient data for model training and test. Following featureselection, various machine learning algorithms were employed, including Decision Tree (DT), Efficient Neural Network (ENet), k-Nearest Neighbor (KNN), Light Gradient Boosting Machine (LightGBM), Multi-Layer Perceptron (MLP), Multinomial Mixture Model (Multinom), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) . A five-fold cross-test strategy was implemented to minimize bias and assess model performance. SHapley Additive exPlanations (SHAP) was used to interpret the results. Results: A total of 6,866 critically ill sepsis patients were analyzed, of whom 5,896 developed AKI during hospitalization The RF model demonstrated superior performance, attaining an average AUC score of 0.89 on the ROC curve. SHAP analysis provided detailed insights into feature importance, including urine output, BMI, SOFA score, and maximum blood urea nitrogen, enhancing the clinical applicability of the model. Conclusion: The machine learning models developed in this study effectively predicted the stages of AKI in severely ill sepsis patients, with the Random Forest model demonstrating optimal performance. SHAP analysis offered crucial insights into the risk factors, facilitating timely and personalized interventions within a clinical setting. Additional multi-center research is essential to confirm the validity of these findings and to ultimately improve patient outcomes and quality of life. KEYWORDS acute kidney injury, MIMIC-IV database, machine learning, prediction model, sepsis Frontiers in Medicine

FAQs

Why is early AKI prediction important in sepsis patients?

AKI during sepsis is associated with substantially higher mortality and a significant disease burden. Early identification enables prompt supportive interventions that can improve prognosis.

What dataset and modeling approach were used in the study?

The study used the MIMIC-IV database to extract sepsis patient records for training and testing. Multiple machine learning models were compared, validated with a five-fold cross-testing strategy.

How did the researchers make the predictions interpretable for clinical use?

SHAP (SHapley Additive exPlanations) was used to interpret results and quantify feature importance. Key influential factors included urine output, BMI, SOFA score, and maximum blood urea nitrogen.

Aug 4, 2026

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