Impact of Machine Learning Prediction on Intraoperative Transfusion in Cranial Operation: Classification, Regression, and Decision Curve Analysis

Abstract

Objective: This study aimed to use machine learning (ML) for the prediction of intraoperative packed red cell (PRC) transfusion and the number of units of transfused PRC, as well as estimate the net benefit of the ML models through decision curve analysis. Methods: The retrospective cohort study was conducted on patients who underwent cranial operations. Clinical data and transfusion data were extracted. Supervised ML algorithms were trained and tested as ML classification for the prediction of intraoperative PRC transfusion and ML regression for predicting the number of transfused PRC units. Results: Out of 2683 patients, 42.9% of neurosurgical patients intraoperatively received PRC. Artificial neural network, gradient boosting classifier, and random forest were the algorithms that had high area under the receiver operating characteristic curve of 0.912, 0.911, and 0.909, respectively, in ML classification, while random forest with regression had the lowest root mean squared error and mean absolute error in ML regression. Conclusions: ML is one of the most effective approaches to developing clinical prediction tools that can enhance the efficiency of blood utilization. Additionally, ML has become a valuable tool in modern health technologies as the computerized clinical decision support systems assist the physician in decision-making in real-world practice.