Machine learning-based prediction of transfusion.

ABSTRACT:

Background

The ability to predict transfusions arising during hospital admission might enable economized blood supply management and might furthermore increase patient safety by ensuring a sufficient stock of red blood cells (RBCs) for a specific patient. We therefore investigated the precision of four different machine learning-based prediction algorithms to predict transfusion, massive transfusion, and the number of transfusions in patients admitted to a hospital.

Study design and methods

This was a retrospective, observational study in three adult tertiary care hospitals in Western Australia between January 2008 and June 2017. Primary outcome measures for the classification tasks were the area under the curve for the receiver operating characteristics curve, the F_1</

SUBMITTER: Mitterecker A 

PROVIDER: S-EPMC7540018 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature