Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs

TL;DR

This paper introduces an ensemble supervised framework using simple study designs and historical data to effectively signal pediatric adverse drug reactions, overcoming ethical constraints of clinical trials.

Contribution

The paper presents a novel ensemble of simple study designs combined with supervised learning to detect pediatric side effects from longitudinal data.

Findings

Achieved a false positive rate of 0.149

Sensitivity of 0.547, specificity of 0.851

Outperformed individual simple study designs

Abstract

Background: Children are frequently prescribed medication off-label, meaning there has not been sufficient testing of the medication to determine its safety or effectiveness. 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. Objective: The objective of this paper is to investigate whether an ensemble of simple study designs can be implemented to signal acutely occurring side effects effectively within the paediatric population by using historical longitudinal data. The majority of pharmacovigilance techniques are unsupervised, but this research presents a supervised framework. 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 classiffier to determine the likelihood of the drug and medical event pair corresponding to an adverse drug reaction. The classiffier 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 effectively.