Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks

TL;DR

This paper uses advanced Bayesian networks to analyze how drug prescriptions impact the progression of multimorbidity, revealing potential adverse effects across different disease groups in primary care data.

Contribution

It introduces the application of tractable Bayesian networks to model complex drug-disease interactions and efficiently identify adverse effects in large medical datasets.

Findings

Prescriptions can negatively influence the development of cardiovascular multimorbidity.

Drug treatments for one disease may adversely affect other disease groups.

Efficient modeling enables large-scale analysis of drug effects on multimorbidity.

Abstract

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.