Author:
Li Xunliang,Wu Ruijuan,Zhao Wenman,Shi Rui,Zhu Yuyu,Wang Zhijuan,Pan Haifeng,Wang Deguang
Abstract
Abstract
This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury (SA-AKI). This study collected data on SA-AKI patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. After employing Lasso regression for feature selection, six ML approaches were used to build the model. The optimal model was chosen based on precision and area under curve (AUC). In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) algorithms. There were 8129 sepsis patients eligible for participation; the median age was 68.7 (interquartile range: 57.2–79.6) years, and 57.9% (4708/8129) were male. After selection, 24 of the 44 clinical characteristics gathered after intensive care unit admission remained linked with prognosis and were utilized developing ML models. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.794. According to the SHAP values, the sequential organ failure assessment score, respiration, simplified acute physiology score II, and age were the four most influential variables in the XGBoost model. Individualized forecasts were clarified using the LIME algorithm. We built and verified ML models that excel in early mortality risk prediction in SA-AKI and the XGBoost model performed best.
Funder
Co-construction project of clinical and preliminary disciplines of Anhui Medical University in 2020
Co-construction project of clinical and preliminary disciplines of Anhui Medical University in 2021
the Natural Science Foundation of Anhui Province
Incubation Program of National Natural Science Foundation of China of The Second Hospital of Anhui Medical University
Clinical Research Incubation Program of The Second Hospital of Anhui Medical University
Publisher
Springer Science and Business Media LLC
Cited by
2 articles.
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