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Managing patients with multimorbidity often results in polypharmacy: theprescription of multiple drugs. However, the long-term effects of specificcombinations of drugs and diseases are typically unknown. In particular, drugsprescribed for one condition may result in adverse effects for the other. Toinvestigate which types of drugs may affect the further progression ofmultimorbidity, we query models of diseases and prescriptions that are learnedfrom primary care data. State-of-the-art tractable Bayesian networkrepresentations, on which such complex queries can be computed efficiently, areemployed for these large medical networks. Our results confirm thatprescriptions may lead to unintended negative consequences in furtherdevelopment of multimorbidity in cardiovascular diseases. Moreover, a drugtreatment for one disease group may affect diseases of another group.

Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks