Title: Demand-driven clustering in relational domains for predicting adverse drug events
Authors: Davis, Jesse ×
Santos Costa, Vitor
Peissig, Peggy
Caldwell, Michael
Berg, Elizabeth
Page, David #
Issue Date: 2012
Host Document: Proceedings of 29th International Conference on Machine Learning pages:1-9
Conference: International Conference on Machine Learning edition:29th location:Edinburgh, Scotland date:June 26th - July 1st
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 fi t 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 fi nd that our approach is more accurate than performing no clustering, pre-clustering, and using
expert-constructed medical heterarchies.
ISBN: 978-1-4503-1285-1
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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