Description
The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared to other pharmacovigilance work, a limited amount of research has been conducted in this space. Recent work has successfully applied a method of deriving distributed vector representations from structured biomedical knowledge, known as Embedding of Semantic Predications (ESP), to the problem of predicting individual drug side effects. In the current paper we extend this work by applying ESP to the problem of predicting polypharmacy side-effects for particular drug combinations, building on a recent reconceptualization of this problem as a network of drug nodes connected by side effect edges. We evaluate ESP embeddings derived from the resulting graph on a side-effect prediction task against a previously reported graph convolutional neural network approach, using the same data and evaluation methods. We demonstrate that ESP models perform similarly or better, while being faster to train, more re-usable, and significantly simpler.
Learning Objective: After participating in this session, the learner should be better able to:
- Understand how neural embedding models such as Embedding of Semantic Predications can be applied to model drug-drug interactions
- Explain how the problem of drug-drug interaction prediction can be expressed as graph model
- Understand the advantages of using Embedding of Semantic Predications over other approaches
Authors:
Hannah Burkhardt (Presenter)
University of Washington
Devika Subramanian, Rice University
Justin Mower, Rice University
Trevor Cohen, University of Washington
Presentation Materials: