Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian
  Networks
  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.
