Managing patients with multimorbidity often results in polypharmacy: the prescription of multiple drugs. However, the long-term effects of specific combinations of drugs and diseases are typically unknown. In particular, drugs prescribed for one condition may result in adverse effects for the other. To investigate which types of drugs may affect the further progression of multimorbidity, we query models of diseases and prescriptions that are learned from primary care data. State-of-the-art tractable Bayesian network representations, on which such complex queries can be computed efficiently, are employed for these large medical networks. Our results confirm that prescriptions may lead to unintended negative consequences in further development of multimorbidity in cardiovascular diseases. Moreover, a drug treatment for one disease group may affect diseases of another group.

对于多病患者进行管理常常导致多药治疗，然而特定组合的药物和疾病的长期影响通常是未知的。从一级医疗保健数据学习疾病和处方模型来研究可能影响多重疾病进一步发展的药物类型。我们使用最先进的可计算复杂查询的可靠贝叶斯网络表示这些大型医学网络。我们的研究结果证实，处方药可能会在心血管疾病的多重疾病进一步发展中产生意外的负面影响。此外，用于一种疾病组的药物疗法可能会影响另一组疾病。

使用容易处理的贝叶斯网络测量多病症下的药物不良反应