title: Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data.
abstract: Computational Drug Repositioning (CDR) is the knowledge discovery process of finding new indications for existing drugs leveraging heterogeneous drug-related data. Longitudinal observational data such as Electronic Health Records (EHRs) have become an emerging data source for CDR. To address the high-dimensional, irregular, subject and time-heterogeneous nature of EHRs, we propose Baseline Regularization (BR) and a variant that extend the one-way fixed effect model, which is a standard approach to analyze small-scale longitudinal data. For evaluation, we use the proposed methods to search for drugs that can lower Fasting Blood Glucose (FBG) level in the Marshfield Clinic EHR. Experimental results suggest that the proposed methods are capable of rediscovering drugs that can lower FBG level as well as identifying some potential blood sugar lowering drugs in the literature.
authors.affiliation: University of Wisconsin-Madison.
authors.forename: Zhaobin
authors.initials: Z
authors.lastname: Kuang
authors.affiliation: Morgridge Institute for Research.
authors.forename: James
authors.initials: J
authors.lastname: Thomson
authors.affiliation: Marshfield Clinic.
authors.forename: Michael
authors.initials: M
authors.lastname: Caldwell
authors.affiliation: Marshfield Clinic.
authors.forename: Peggy
authors.initials: P
authors.lastname: Peissig
authors.affiliation: Morgridge Institute for Research.
authors.forename: Ron
authors.initials: R
authors.lastname: Stewart
authors.affiliation: University of Wisconsin-Madison.
authors.forename: David
authors.initials: D
authors.lastname: Page