Investigating potential causes of Sepsis with Bayesian network structure learning

TL;DR

This study uses Bayesian network structure learning combining clinical expertise and data-driven methods to identify potential causes of Sepsis, with implications for policy and prediction accuracy.

Contribution

It introduces a novel model averaging and knowledge-based constraint approach for causal inference in Sepsis research.

Findings

Risk factors like COPD, alcohol dependence, and diabetes increase Sepsis likelihood.

The model achieved around 70% accuracy, sensitivity, and specificity.

The AUC of 80% indicates good predictive performance.

Abstract

Sepsis is a life-threatening and serious global health issue. This study combines knowledge with available hospital data to investigate the potential causes of Sepsis that can be affected by policy decisions. We investigate the underlying causal structure of this problem by combining clinical expertise with score-based, constraint-based, and hybrid structure learning algorithms. A novel approach to model averaging and knowledge-based constraints was implemented to arrive at a consensus structure for causal inference. The structure learning process highlighted the importance of exploring data-driven approaches alongside clinical expertise. This includes discovering unexpected, although reasonable, relationships from a clinical perspective. Hypothetical interventions on Chronic Obstructive Pulmonary Disease, Alcohol dependence, and Diabetes suggest that the presence of any of these risk factors in patients increases the likelihood of Sepsis. This finding, alongside measuring the effect of these risk factors on Sepsis, has potential policy implications. Recognising the importance of prediction in improving health outcomes related to Sepsis, the model is also assessed in its ability to predict Sepsis by evaluating accuracy, sensitivity, and specificity. These three indicators all had results around 70%, and the AUC was 80%, which means the causal structure of the model is reasonably accurate given that the models were trained on data available for commissioning purposes only.