Arjen Hommersom

Raphaela Butz, Renée Schulz, Arjen Hommersom et al.

Explaining predictions from Bayesian networks, for example to physicians, is non-trivial. Various explanation methods for Bayesian network inference have appeared in literature, focusing on different aspects of the underlying reasoning. While there has been a lot of technical research, there is very little known about how well humans actually understand these explanations. In this paper, we present ongoing research in which four different explanation approaches were compared through a survey by asking a group of human participants to interpret the explanations.

Jessa Bekker, Arjen Hommersom, Martijn Lappenschaar et al.

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.