广州医药2025，Vol.56Issue(11)：1501-1510,1524,11.DOI:10.20223/j.cnki.1000-8535.2025.11.005
基于机器学习的脓毒症谵妄患者死亡预测模型的构建与评估
Machine learning prediction model for sepsis-associated delirium mortality
HUANG Yiwen 1HU Bei2
作者信息
- 1. Medical College,Shantou University,Shantou 515041,China
- 2. Emergency Department,Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences),Southern Medical University,Guangzhou 510080,China
- 折叠
摘要
Abstract
Objective To construct a 30-day mortality prediction model for patients with sepsis-associated delirium using machine learning methods and identify key predictive factors.Methods A retrospective cohort study was conducted based on the Medical Information Mart for Intensive Care Ⅳ database.Important features were selected using the Boruta algorithm,and models including Decision Tree,K-Nearest Neighbors,LightGBM,Random Forest,Support Vector Machine,and XGBoost were constructed and analyzed.Model performance was evaluated using the area under the reciver operater characteristic(ROC)curve(AUC),along with F1 score,recall,precision,specificity,sensitivity,and positive predictive value.Results The XGBoost model demonstrated strong predictive performance,with AUC values of 0.906 in the training set and 0.762 in the test set.Key predictors identified included admission age,red blood cell distribution width,and white blood cell count.Conclusions The machine learning-based prediction model for sepsis-associated delirium prognosis exhibits robust predictive efficacy,providing a valuable tool for early clinical intervention.关键词
脓毒症/谵妄/机器学习/死亡Key words
sepsis/delirium/machine learning/mortality引用本文复制引用
HUANG Yiwen,HU Bei..基于机器学习的脓毒症谵妄患者死亡预测模型的构建与评估[J].广州医药,2025,56(11):1501-1510,1524,11.
