Demand-driven clustering in relational domains for predicting adverse drug events
Davis, Jesse × Santos Costa, Vitor Peissig, Peggy Caldwell, Michael Berg, Elizabeth Page, David #
Proceedings of 29th International Conference on Machine Learning pages:1-9
International Conference on Machine Learning edition:29th location:Edinburgh, Scotland date:June 26th - July 1st
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.