A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barre Syndrome: Development and Validation Study.

ABSTRACT:

Background

Identifying the key factors of Guillain-Barré syndrome (GBS) and predicting its occurrence are vital for improving the prognosis of patients with GBS. However, there are scarcely any publications on a forewarning model of GBS. A Bayesian network (BN) model, which is known to be an accurate, interpretable, and interaction-sensitive graph model in many similar domains, is worth trying in GBS risk prediction.

Objective

The aim of this study is to determine the most significant factors of GBS and further develop and validate a BN model for predicting GBS risk.

Methods

Large-scale influenza vaccine postmarketing surveillance data, including 79,165 US (obtained from the Vaccine Adverse Event Reporting System between 1990 and 2017) and 12,495 European (obtained fr

SUBMITTER: Huang Y 

PROVIDER: S-EPMC8994148 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature