Identifying Adverse Drug Events by Relational Learning.
Proc AAAI Conf Artif Intell
; 2012: 790-793, 2012 Jul.
Article
en En
| MEDLINE
| ID: mdl-24955289
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 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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Proc AAAI Conf Artif Intell
Año:
2012
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Article