Establishment and evaluation of intraoperative blood transfusion prediction model based on random forest algorithm
10.13303/j.cjbt.issn.1004-549x.2022.07.009
- VernacularTitle:基于随机森林算法的术中输血预测模型的建立与评价
- Author:
Limei FANG
1
;
Ting ZHAGN
1
Author Information
1. Department of Blood Transfusion, First Medical Center of the Chinese People′s Liberation Army General Hospital, Beijing 100853, China
- Publication Type:Journal Article
- Keywords:
intraoperative blood transfusion;
blood transfusion prediction model;
blood transfusion risk factors;
random forest algorithm;
machine learning
- From:
Chinese Journal of Blood Transfusion
2022;35(7):715-719
- CountryChina
- Language:Chinese
-
Abstract:
【Objective】 To predict the risk factors of intraoperative blood transfusion by establishing a random forest algorithm prediction model, and to evaluate its prediction performance in clinical. 【Methods】 A total of 48 176 patients who underwent surgery from January 2014 to December 2017 in the First Medical Center of the Chinese People′s Liberation Army General Hospital were collected and divided into a blood transfusion group(n=5 035) and a non-transfusion group(n=43 141) according to whether blood was transfused or not during the operation, and the age, gender, weight, blood routine, coagulation test indicators, surgical grade, number of operations and anesthesia methods, and preoperative blood transfusion history between the two groups were compared and analyzed. All cases were randomly divided into training set(n=33 723) and the test set(n=14 453), using the sklearn function package in the computer programming language(Python V 3.9.0) to introduce the random forest algorithm, with 2 groups of different factors incorporated into the random forest algorithm to build the model, and the model was evaluated using the operating curve(ROC). 【Results】 1) There were statistically significant differences between the blood transfusion group and the non-transfusion group in terms of gender, age, blood routine, coagulation function, surgical grade, and preoperative blood transfusion history(P<0.05); 2) Except for the distribution of blood types, there was no statistically significant difference in other indicators between the test set and the training set(P>0.05); 3) In the established intraoperative blood model, the blood routine, coagulation function and general anesthesia had a great influence, with the cumulative importance > " 0.90" ; 4) The ROC analysis showed that the area under the ROC curve of the random forest model was 0.91 and 0.82 in the training set and the test set, which demonstrated a good predictive ability. 【Conclusion】 The intraoperative blood, using prediction model based on random forest method, can predict intraoperative blood use and blood transfusion risk factors.
