Investigating potential causes of Sepsis with Bayesian network structure learning

• URL: http://arxiv.org/abs/2406.09207v1
• Date: Thu, 13 Jun 2024 15:08:44 GMT
• Title: Investigating potential causes of Sepsis with Bayesian network structure learning
• Authors: Bruno Petrungaro, Neville K. Kitson, Anthony C. Constantinou,
• Abstract summary: This study combines knowledge with available hospital data to investigate the potential causes of Sepsis. We investigate the underlying causal structure of this problem by combining clinical expertise with score-based, constraint-based, and hybrid structure learning algorithms.
• Score: 5.953513005270839
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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