Intelligent prediction of RBC demand in trauma patients using decision tree methods.

ABSTRACT:

Background

The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors' experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.

Methods

A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusi

SUBMITTER: Feng YN 

PROVIDER: S-EPMC8142481 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature