Predicting High-Order Directional Drug-Drug Interaction Relations

High-order Drug-Drug Interactions (DDI) are common particularly for elderly people. It is highly non-trivial to detect such interactions via in vivo/in vitro experiments. In this paper, we present SVM-based classification methods to predict whether a high-order directional drug-drug interaction (HoDDDI) instance is associated with adverse drug reactions (ADRs) and induced side effects. Specifically, we developed kernels for HoDDDI instances of arbitrary orders that are constructed from various single-drug information. The experiments over datasets extracted from electronic health records demonstrate that our classification methods can achieve the best F1 as 0.793.