Signalling paediatric side effects using an ensemble of simple study designs
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...
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| Format: | Article |
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Springer
2014
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| Online Access: | https://eprints.nottingham.ac.uk/3356/ |
| _version_ | 1848791022456274944 |
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| author | Reps, Jenna M. Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. |
| author_facet | Reps, Jenna M. Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. |
| author_sort | Reps, Jenna M. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:21:54Z |
| format | Article |
| id | nottingham-3356 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:21:54Z |
| publishDate | 2014 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-33562020-05-04T16:42:32Z https://eprints.nottingham.ac.uk/3356/ Signalling paediatric side effects using an ensemble of simple study designs Reps, Jenna M. Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. 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. Springer 2014-03-01 Article PeerReviewed Reps, Jenna M., Garibaldi, Jonathan M., Aickelin, Uwe, Soria, Daniele, Gibson, Jack E. and Hubbard, Richard B. (2014) Signalling paediatric side effects using an ensemble of simple study designs. Drug Safety, 37 (3). pp. 163-170. ISSN 0114-5916 Biomedical Informatics Data Mining http://link.springer.com/article/10.1007/s40264-014-0137-z doi:10.1007/s40264-014-0137-z doi:10.1007/s40264-014-0137-z |
| spellingShingle | Biomedical Informatics Data Mining Reps, Jenna M. Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. Signalling paediatric side effects using an ensemble of simple study designs |
| title | Signalling paediatric side effects using an ensemble of simple study designs |
| title_full | Signalling paediatric side effects using an ensemble of simple study designs |
| title_fullStr | Signalling paediatric side effects using an ensemble of simple study designs |
| title_full_unstemmed | Signalling paediatric side effects using an ensemble of simple study designs |
| title_short | Signalling paediatric side effects using an ensemble of simple study designs |
| title_sort | signalling paediatric side effects using an ensemble of simple study designs |
| topic | Biomedical Informatics Data Mining |
| url | https://eprints.nottingham.ac.uk/3356/ https://eprints.nottingham.ac.uk/3356/ https://eprints.nottingham.ac.uk/3356/ |