Predicting the causative pathogen among children with pneumonia using a causal Bayesian network.

ABSTRACT:

Background

Pneumonia remains a leading cause of hospitalization and death among young children worldwide, and the diagnostic challenge of differentiating bacterial from non-bacterial pneumonia is the main driver of antibiotic use for treating pneumonia in children. Causal Bayesian networks (BNs) serve as powerful tools for this problem as they provide clear maps of probabilistic relationships between variables and produce results in an explainable way by incorporating both domain expert knowledge and numerical data.

Methods

We used domain expert knowledge and data in combination and iteratively, to construct, parameterise and validate a causal BN to predict causative pathogens for childhood pneumonia. Expert knowledge elicitation occurred through a series of group workshops,

SUBMITTER: Wu Y 

PROVIDER: S-EPMC10035934 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature