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.

翻译：脓毒症是一种危及生命的严重全球性健康问题。本研究结合专业知识和现有医院数据，旨在探究可能受政策决策影响的脓毒症潜在病因。我们通过整合临床专业知识与基于评分、基于约束及混合结构学习算法，深入研究了该问题的潜在因果结构。研究采用了一种新颖的模型平均与知识约束相结合的方法，以达成用于因果推断的共识结构。结构学习过程凸显了在临床专业知识基础上探索数据驱动方法的重要性，这包括从临床角度发现意外但合理的关联关系。针对慢性阻塞性肺疾病、酒精依赖及糖尿病的假设性干预表明，患者存在任一这些风险因素都会增加罹患脓毒症的可能性。这一发现连同对这些风险因素影响脓毒症的量化评估，具有潜在的政策启示意义。认识到预测在改善脓毒症相关健康结局中的重要性，本研究还对所建模型的脓毒症预测能力进行了评估。共识模型生成的预测结果在准确性、敏感性和特异性方面均接受了检验，这三项指标结果均约为70%，AUC达到80%。鉴于模型仅基于现有委托用途数据进行训练，该结果表明模型的因果结构具有合理的准确性。