  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 Sepsis related health
outcomes, the model built is also assessed in its ability to predict Sepsis.
The predictions generated by the consensus model were assessed for their
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.
