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Background: Children are frequently prescribed medication off-label, meaningthere has not been sufficient testing of the medication to determine its safetyor effectiveness. The main reason this safety knowledge is lacking is due toethical restrictions that prevent children from being included in the majorityof clinical trials. Objective: The objective of this paper is to investigatewhether an ensemble of simple study designs can be implemented to signalacutely occurring side effects effectively within the paediatric population byusing historical longitudinal data. The majority of pharmacovigilancetechniques are unsupervised, but this research presents a supervised framework.Methods: Multiple measures of association are calculated for each drug andmedical event pair and these are used as features that are fed into aclassiffier to determine the likelihood of the drug and medical event paircorresponding to an adverse drug reaction. The classiffier is trained usingknown 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 areference set of drug and medical event pairs. The novel framework consistentlyoutperformed each individual simple study design. Conclusion: This researchshows that it is possible to exploit the mechanism of causality and presents aframework for signalling adverse drug reactions effectively.

Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs