中国输血杂志2025，Vol.38Issue(11)：1488-1494,7.DOI:10.13303/j.cjbt.issn.1004-549x.2025.11.003
基于机器学习模型的上消化道出血患者的红细胞输血评估分析
Evaluation of red blood cell transfusion in patients with upper gastrointestinal bleeding using machine learning models
摘要
Abstract
Objective To comprehensively evaluate and analyze the transfusion outcomes of patients with acute upper gastrointestinal bleeding(UGIB).Methods The transfusion management system and hospital information system(HIS)were used to retrospectively collect clinical data of 230 patients with UGIB admitted to Zhejiang Provincial People's Hospital and its branches from June 2018 to June 2021.101 cases were screened and categorized into transfusion group(n=56)and non-transfusion group(n=45)based on transfusion outcomes.The cohort comprised 68 males and 33 females.A univariate model based on the AIMS65 score,a logistic multiple regression model,and multivariate transfusion models using machine learning methods(including Random Forest,Support Vector Machine,and Artificial Neural Network)were established.The sensitivity,specificity,accuracy,and receiver operating characteristic(ROC)curves of each model were compared.Results For the univariate model based on the AIMS65 scoring,the optimal threshold was 1.5.This model demonstrated a sensitivity of 0.446,a specificity of 0.822,an AUC of 0.67,an accuracy(ACC)of 0.614,a Kappa value of 0.256,and an F1-score of 0.655.For logistics regression model(optimal critical probability:0.459),the sensitivity was 0.929,speci-ficity was 0.889,AUC was 0.96,ACC was 0.911,Kappa was 0.819,and F1-score was 0.899.For the Random Forest model(optimal critical probability:0.458),the sensitivity was 0.964,specificity was 0.956,AUC was 0.99,ACC was 0.960,Kappa was 0.920,and F1-score was 0.956.For the Support Vector Machine model(optimal critical probability:0.474),the sensitivity was 0.875,specificity was 0.933,AUC was 0.94,ACC was 0.901,Kappa was 0.801,and F1-score was 0.894.For the Artificial Neural Network model(optimal critical probability:0.797),the sensitivity was 0.804,specificity was 0.956,AUC was 0.96,ACC was 0.871,Kappa was 0.745,and F1-score was 0.869.Ten-fold cross vali-dation also confirmed the reliability of the results.Conclusion Based on integrated various clinical test indicators of pa-tients,we could establish logistic regression model and multiple machine learning models.These models hold significant val-ue for predicting the need for blood transfusion in patients,indicating a promising application prospect for machine learning algorithms in transfusion prediction.关键词
上消化道出血/AIMS65/逻辑回归/机器学习/多因素输血模型Key words
upper gastrointestinal bleeding/AIMS65/logistic regression/machine learning/multivariate transfusion models分类
医药卫生引用本文复制引用
杜垚强,章碧琴,徐怡琳,陈秉宇,胡卫国..基于机器学习模型的上消化道出血患者的红细胞输血评估分析[J].中国输血杂志,2025,38(11):1488-1494,7.基金项目
浙江省医药卫生科技计划项目(2024KY020) （2024KY020）
浙江省卫生高层次人才计划(2023 年度浙江省医坛新秀) （2023 年度浙江省医坛新秀）
