机器学习模型在肝移植手术用血分析预测中的应用

Application of machine learning in predictive analysis of blood usage for liver transplantation surgery

目的 探讨机器学习在肝移植手术前科学合理备血及手术用血分析预测中的应用。方法 收集356例肝移植手术患者的性别、年龄、临床诊断、手术方式等临床基本信息,收集手术时长(Time)和术前血红蛋白(Hb)、红细胞压积(Hct)、血小板计数(Plt)、凝血酶原时间(PT)、活化部分凝血活酶时间(APTT)、纤维蛋白原(Fib)、总胆红素(TBIL)、白蛋白(ALB)、肌酐(Crea)、总蛋白(TP)的检验结果以及术中输血量,应用Python机器语言建立能够预测肝移植手术大量输血风险的机器学习模型,并对模型进行评价,选择出最优预测模型。结果 对构建的7个机器学习模型评价,其中线性回归模型(logistic regression)表现最佳(AUROC:0.90,F1得分:0.82),准确度79.44%,精密度79.69%;随机森林(random forest classifier)表现次佳(AUROC:0.87,F1得分:0.83),准确度79.44%,精密度77.94%。结论 通过运行Python机器语言建立机器学习预测模型,对科学合理备血和大量输血风险预测,保证肝移植手术用血安全具有重要临床意义。

Objective To explore the application of machine learning in scientific and rational blood preparation and predictive analysis for surgical blood usage before liver transplantation surgery.Methods Clinical basic information including gender,age,clinical diagnosis and surgical methods of 356 liver transplantation patients were collected.The duration(Time)and preoperative laboratory test results of hemoglobin(Hb),hematocrit(Hct),platelet count(Plt),prothrombin time(PT),activated partial thromboplastin time(APTT),fibrinogen(Fib),total bilirubin(TBIL),albumin(ALB),creatinine(Crea)and total protein(TP),as well as the amount of intraoperative blood transfusion were collected.A machine learning model capable of predicting the risk of massive blood transfusion during liver transplantation surgery was established by Python,and was evaluated to select the optimal predictive model.Results Among the 7 machine learning models constructed,the logistic regression model performed the best(AUROC:0.90,F1 score:0.82),with an accuracy of 79.44%and precision of 79.69%,followed by the random forest classifier(AUROC:0.87,F1 score:0.83),with an accuracy of 79.44%and precision of 77.94%.Conclusion Establishing a machine learning prediction model by Python is of significant clinical importance for scientific blood preparation,predicting the risk of massive blood transfusion and ensuring the safety of blood use in liver transplantation surgery.