Machine Learning Model for Perioperative Transfusion Prediction

Overview

This study aimed to develop and interpret a machine learning model to predict red blood cell (RBC) transfusion.

A dataset from a multicenter study involving 6121 patients underwent elective major surgery was analysed. Data concerning patients who received inappropriate RBC transfusion were excluded. Twenty one perioperative features were used to predict RBC transfusion. The data set was randomly split into train and validation sets (70-30). Decision tree, random forest, k-nearest neighbors, logistic regression, and eXtreme garadient boosting (XGBoost) methods were used for prediction. The area under the curves (AUC) of the receiver operating characteristics curves for the machine learning models used for RBC transfusion prediction were compared.

Conditions

Interventions

Eligibility

Locations (1)

Outcomes