Development and validation of a Bayesian belief network predicting the probability of blood transfusion after pediatric injury.

ABSTRACT:

Background

Early recognition and intervention of hemorrhage are associated with decreased morbidity in children. Triage models have been developed to aid in the recognition of hemorrhagic shock after injury but require complete data and have limited accuracy. To address these limitations, we developed a Bayesian belief network, a machine learning model that represents the joint probability distribution for a set of observed or unobserved independent variables, to predict blood transfusion after injury in children and adolescents.

Methods

We abstracted patient, injury, and resuscitation characteristics of injured children and adolescents (age 1 to 18 years) from the 2017 to 2019 Trauma Quality Improvement Project database. We trained a Bayesian belief network to predict blood

SUBMITTER: Sullivan TM 

PROVIDER: S-EPMC9748028 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature