Drug Recommendation toward Safe Polypharmacy
This addresses a public health issue for patients on multiple medications, but it is incremental as it builds on existing drug recommendation methods with a new joint modeling approach.
The paper tackled the problem of adverse drug reactions from high-order drug-drug interactions in polypharmacy by developing a joint model for recommending to-avoid drugs and predicting ADR labels, demonstrating strong performance on real datasets.
Adverse drug reactions (ADRs) induced from high-order drug-drug interactions (DDIs) due to polypharmacy represent a significant public health problem. In this paper, we formally formulate the to-avoid and safe (with respect to ADRs) drug recommendation problems when multiple drugs have been taken simultaneously. We develop a joint model with a recommendation component and an ADR label prediction component to recommend for a prescription a set of to-avoid drugs that will induce ADRs if taken together with the prescription. We also develop real drug-drug interaction datasets and corresponding evaluation protocols. Our experimental results on real datasets demonstrate the strong performance of the joint model compared to other baseline methods.