Machine Learning Versus Traditional Scores in Predicting Erythrocyte Need

The values in the ML algorithm were selected according to logistic regression analysis and the values used in the other six scores tested. The success rate of the constructed networks correct predictions was considered as the success rate of the algorithm. The usefulness of the test was determined through AUROC analysis. Two algorithms were tested in our study. In the first algorithm (ML1), the dependent variable was erythrocyte suspension (ES) consumption, and the independent variables included patients; demographic data, laboratory data, and operational data

is an Ml algorithm created by combining commonly used bleeding scores

ACTION CRUSCADE TRACK WILL-BLEED PAPWORTH TRUST ACTAPORT skores used to predict ES need