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among children with pneumonia using a causal Bayesian network

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.

Citation:
Wu Y, Mascaro S, Bhuiyan M, Fathima P, Mace AO, Nicol MP, Richmond PC, Kirkham LA, Dymock M, Foley DA, McLeod C, Borland ML, Martin A, Williams PCM, Marsh JA, Snelling TL, Blyth CC. Predicting the causative pathogen among children with pneumonia using a causal Bayesian network. PLoS Comput Biol. 2023;19(3):e1010967.

Keywords:
Anti-Bacterial Agents; Bayes Theorem; Pneumonia; Surveys and Questionnaires; anti-infective agent

Abstract:
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.