title: Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events.
abstract: 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.
authors.affiliation: KU Leuven, Celestijnenlaan 200a, Heverlee 3001, Belgium.
authors.forename: Jesse
authors.initials: J
authors.lastname: Davis
authors.affiliation: CRACS INESC-TEC and FCUP Universidade do Porto, Rua do Campo Alegre, 4169-007 PORTO, Portugal.
authors.forename: Vítor Santos
authors.initials: VS
authors.lastname: Costa
authors.affiliation: Marshfield Clinic, 1000 N Oak Ave, Marshfield, WI 54449 USA.
authors.forename: Peggy
authors.initials: P
authors.lastname: Peissig
authors.affiliation: Marshfield Clinic, 1000 N Oak Ave, Marshfield, WI 54449 USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Caldwell
authors.affiliation: University of Wisconsin - Madison, 1300 University Avenue, Madison, WI 53706 USA.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Berg
authors.affiliation: University of Wisconsin - Madison, 1300 University Avenue, Madison, WI 53706 USA.
authors.forename: David
authors.initials: D
authors.lastname: Page