AAAI Publications, Twenty-Sixth AAAI Conference on Artificial Intelligence

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Identifying Adverse Drug Events by Relational Learning
David Page, Vitor Santos Costa, Sriraam Natarajan, Aubrey Barnard, Peggy Peissig, Michael Caldwell

Last modified: 2012-07-14

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, postmarketing surveillance of drugs to identify previously-unanticipated ADEs. This paper casts this problem as a reverse machine learning task, related to relational subgroup discovery and provides an initial evaluation of this approach based on experiments with an actual EMR/EHR and known adverse drug events.

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