ICU患者急性肾损伤发生风险的LightGBM预测模型

张渊; 冯聪; 李开源; 张政波; 曹德森; 黎檀实

doi:10.3969/j.issn.2095-5227.2019.04.004

ICU患者急性肾损伤发生风险的LightGBM预测模型

LightGBM model for predicting acute kidney injury risk in ICU patients

摘要

摘要:
目的基于机器学习模型LightGBM构建ICU患者发生急性肾损伤(acute kidney injury，AKI)的预测模型，为临床医护人员提供辅助决策支持。
方法采用公开的大型ICU数据库重症监护医学信息数据库(MIMIC-Ⅲ)作为数据集，提取1 166例患者，其中男性513例(44.00%)，中位年龄70.93岁，75.8%(884例)的患者发展为AKI。以患者入ICU时的生理生化指标为预测变量，预测患者24 h后是否发展为AKI。采用LightGBM构建预测模型，并与logistic回归及随机森林模型进行对比，采用五折交叉验证评价模型性能。
结果结果显示，LightGBM模型对AKI预测的准确率为0.89，AUC为0.92；logistic回归模型和随机森林模型的AUC分别为0.75和0.89，准确率为0.84和0.86。
结论 LightGBM在AKI预测模型中表现最优，采用患者入ICU时的生理生化指标，预测模型准确率和AUC可达0.89和0.92。

Abstract:
Objective To develop a predictive model for acute kidney injury (AKI) in critically ill patients based on LightGBM and provide support for clinical decision.
Methods An open source critical care database, MIMIC-Ⅲ was used in this study. A total of 1 166 patients were included in this study with median age of 70.93 years, of which 513 cases (44.00%) were male, and 884 of them had developed AKI (75.8%). To predict the occurrence of AKI after 24 hours, the model was built utilizing predictors including laboratory tests and vital signs measured at ICU admission. LightGBM, logistic regression, and random forest models were established to predict the risk of AKI, and their predictive performances were evaluated using five-fold cross-validation.
Results The accuracy of LightGBM was 0.89, and the AUC was 0.92. However, the accuracy of logistic regression and random forest were 0.84 and 0.86, and their AUC were 0.75 and 0.89, respectively.
Conclusion LightGBM performs well in predicting AKI at 24 hours after ICU admission. The accuracy and AUC of LightGBM model are up to 0.89 and 0.92.

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