Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events
This addresses the challenge of handling latent structure in relational medical data for better prediction of adverse drug reactions, representing an incremental improvement in statistical relational learning.
The paper tackled the problem of predicting adverse drug events from electronic medical records by introducing a demand-driven clustering approach during learning, which improved accuracy over existing methods like no clustering, pre-clustering, and expert-constructed heterarchies.
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connections between related medications or diseases. One way to capture the latent structure is via a relational clustering of objects. We propose a novel approach that, instead of pre-clustering the objects, performs a demand-driven clustering during learning. We evaluate our algorithm on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication. We find that our approach is more accurate than performing no clustering, pre-clustering, and using expert-constructed medical heterarchies.