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Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs

Reps, Jenna; M. Garibaldi, Jonathan; Aickelin, Uwe; Soria, Daniele; E. Gibson, Jack; B. Hubbard, Richard

Authors

Jenna Reps

Uwe Aickelin

Daniele Soria

JACK GIBSON jack.gibson@nottingham.ac.uk
Assistant Professor in Epidemiology

RICHARD HUBBARD richard.hubbard@nottingham.ac.uk
Blf/Gsk Professor of Epidemiological Resp Research



Abstract

Background: Children are frequently prescribed medication `o-label', meaning there has not been sucient testing of the medication to determine its safety or eectiveness. The main reason this safety knowledge is lacking is due to
ethical restrictions that prevent children from being included in the majority of clinical trials.

Methods: Multiple measures of association are calculated for each drug and medical event pair and these are used as features that are fed into a classifier to determine the likelihood of the drug and medical event pair corresponding to an adverse drug reaction. The classier is trained using known adverse drug reactions or known non-adverse drug reaction relationships.

Results: The novel ensemble framework obtained a false positive rate of 0:149, a sensitivity of 0:547 and a specificity of 0:851 when implemented on a reference set
of drug and medical event pairs. The novel framework consistently outperformed each individual simple study design.

Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions eectively.

Citation

Reps, J., M. Garibaldi, J., Aickelin, U., Soria, D., E. Gibson, J., & B. Hubbard, R. (2014). Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs. Drug Safety, 37(3), 163-170. https://doi.org/10.1007/s40264-014-0137-z

Journal Article Type Article
Online Publication Date Feb 19, 2014
Publication Date 2014-03
Deposit Date Sep 29, 2014
Publicly Available Date Sep 29, 2014
Journal Drug Safety
Print ISSN 0114-5916
Electronic ISSN 1179-1942
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 37
Issue 3
Pages 163-170
DOI https://doi.org/10.1007/s40264-014-0137-z
Keywords Biomedical Informatics, Data Mining
Public URL http://eprints.nottingham.ac.uk/id/eprint/3356
Publisher URL http://link.springer.com/article/10.1007/s40264-014-0137-z
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information The final publication is available at Springer via http://dx.doi.org/10.1007/s40264-014-0137-z

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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