title: Identifying Adverse Drug Events by Relational Learning.
abstract: The pharmaceutical industry, consumer protection groups, users of medications and government oversight agencies are all strongly interested in identifying adverse reactions to drugs. While a clinical trial of a drug may use only a thousand patients, once a drug is released on the market it may be taken by millions of patients. As a result, in many cases adverse drug events (ADEs) are observed in the broader population that were not identified during clinical trials. Therefore, there is a need for continued, post-marketing surveillance of drugs to identify previously-unanticipated ADEs. This paper casts this problem as areverse machine learning task, related torelational subgroup discoveryand provides an initial evaluation of this approach based on experiments with an actual EMR/EHR and known adverse drug events.
authors: University of Wisconsin-Madison.
authors: David
authors: D
authors: Page
authors: CRACS-INESC TEC and FCUP.
authors: Vítor Santos
authors: VS
authors: Costa
authors: Wake Forest University.
authors: Sriraam
authors: S
authors: Natarajan
authors: University of Wisconsin-Madison.
authors: Aubrey
authors: A
authors: Barnard
authors: Marshfield Clinic Research Foundation.
authors: Peggy
authors: P
authors: Peissig
authors: Marshfield Clinic.
authors: Michael
authors: M
authors: Caldwell