Shi Huanhuan, Shen Yuting, Li Lu
Department of Gastroenterology, Peking University Third Hospital, Beijing, China.
Department of Internal Medicine, Wuhan University of Technology Hospital, Wuhan, China.
Front Med (Lausanne). 2023 Aug 31;10:1221602. doi: 10.3389/fmed.2023.1221602. eCollection 2023.
Acute kidney injury (AKI) is a common and important complication in patients with gastrointestinal bleeding who are admitted to the intensive care unit. The present study proposes an artificial intelligence solution for acute kidney injury prediction in patients with gastrointestinal bleeding admitted to the intensive care unit.
Data were collected from the eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The prediction model was developed using the extreme gradient boosting (XGBoost) model. The area under the receiver operating characteristic curve, accuracy, precision, area under the precision-recall curve (AUC-PR), and F1 score were used to evaluate the predictive performance of each model.
Logistic regression, XGBoost, and XGBoost with severity scores were used to predict acute kidney injury risk using all features. The XGBoost-based acute kidney injury predictive models including XGBoost and XGBoost+severity scores model showed greater accuracy, recall, precision AUC, AUC-PR, and F1 score compared to logistic regression.
The XGBoost model obtained better risk prediction for acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit than the traditional logistic regression model, suggesting that machine learning (ML) techniques have the potential to improve the development and validation of predictive models in patients with gastrointestinal bleeding admitted to the intensive care unit.
急性肾损伤(AKI)是入住重症监护病房的胃肠道出血患者常见且重要的并发症。本研究提出了一种用于预测入住重症监护病房的胃肠道出血患者急性肾损伤的人工智能解决方案。
从电子重症监护协作研究数据库(eICU-CRD)和重症监护医学信息集市-IV(MIMIC-IV)数据库收集数据。使用极端梯度提升(XGBoost)模型开发预测模型。采用受试者操作特征曲线下面积、准确率、精确率、精确召回率曲线下面积(AUC-PR)和F1分数来评估每个模型的预测性能。
使用逻辑回归、XGBoost以及结合严重程度评分的XGBoost,利用所有特征预测急性肾损伤风险。与逻辑回归相比,基于XGBoost的急性肾损伤预测模型,包括XGBoost和XGBoost+严重程度评分模型,显示出更高的准确率、召回率、精确率AUC、AUC-PR和F1分数。
与传统逻辑回归模型相比,XGBoost模型在预测入住重症监护病房的胃肠道出血患者急性肾损伤风险方面表现更佳,这表明机器学习(ML)技术有潜力改进入住重症监护病房的胃肠道出血患者预测模型的开发和验证。