Development of a machine learning approach for prediction of red blood cell transfusion in patients undergoing Cesarean section at a single institution.

ABSTRACT: Despite recent advances in surgical techniques and perinatal management in obstetrics for reducing intraoperative bleeding, blood transfusion may occur during a cesarean section (CS). This study aims to identify machine learning models with an optimal diagnostic performance for intraoperative transfusion prediction in parturients undergoing a CS. Additionally, to address model performance degradation due to data imbalance, this study further investigated the variation in predictive model performance depending on the ratio of event to non-event data (1:1, 1:2, 1:3, and 1:4 model datasets and raw data).The area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) were evaluated to compare the predictive accuracy of different machine lear

SUBMITTER: Lee SW 

PROVIDER: S-EPMC11258332 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature