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, surveys and one-on-one meetings involving 6-8 experts from diverse domain areas. The model performance was evaluated based on both quantitative metrics and qualitative expert validation. Sensitivity analyses were conducted to investigate how the target output is influenced by varying key assumptions of particular high degree of uncertainty around data or domain expert knowledge. Results Designed to apply to a cohort of children with X-ray confirmed pneumonia who presented to a tertiary paediatric hospital in Australia, the resulting BN offers explainable and quantitative predictions on a range of variables of interest, including the diagnosis of bacterial pneumonia, detection of respiratory pathogens in the nasopharynx, and the clinical phenotype of a pneumonia episode. Satisfactory numeric performance has been achieved including an area under the receiver-operating characteristics curve of 0.8 in predicting clinically-confirmed bacterial pneumonia with sensitivity 88% and specificity 66% given certain input scenarios (i.e., information that is available and entered into the model) and trade-off preferences (i.e., relative weightings of the consequences of false positive versus false negative predictions). We specifically highlight that a desirable model output threshold for practical use is very dependent upon different input scenarios and trade-off preferences. Three commonly encountered scenarios were presented to demonstrate the potential usefulness of the BN outputs in various clinical pictures. Conclusions To our knowledge, this is the first causal model developed to help determine the causative pathogen for paediatric pneumonia. We have shown how the method works and how it would help decision making on the use of antibiotics, providing insight into how computational model predictions may be translated to actionable decisions in practice. We discussed key next steps including external validation, adaptation and implementation. Our model framework and the methodological approach can be adapted beyond our context to broad respiratory infections and geographical and healthcare settings.

Active Data Selection and Information Seeking

“…This is useful in that it suggests efficient message passing schemes that may be used to perform an inference (see [20,21] for overviews). Such representations have broad applicability, ranging from clinical reasoning [22] to environmental conservation [23]. For our purposes, message passing in graphical models is useful in that it helps us to find the quantities required for computing expected information gain.…”

Clinical Indicator Analysis for Predicting Pathogenic Pneumonia Infection in Newborns with Distributed Sensor Networks Data Analytics

“…The advancement of tools and techniques for data analytics has allowed the doctors to achieve an early prediction, and with the auto-generated outcomes, the changing of the medical procedures for decreasing the mortality of the patients can be obtained [33]. Such a fact proves to be extremely desirable for increasing the overall experience of the patients and for inducing a greater chance of reaching the desired outcome [28].…”