A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations

Reps, Jenna M. and Garibaldi, Jonathan M. and Aickelin, Uwe and Gibson, Jack E. and Hubbard, Richard B. (2015) A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations. Journal of Biomedical Informatics, 56 . pp. 356-368. ISSN 1532-0480

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Abstract

Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data.

In this paper we propose using a set of causality considerations developed by the epidemiologist Bradford Hill as a basis for engineering features that enable the application of supervised learning for the problem of detecting negative side effects. The Bradford Hill considerations look at various perspectives of a drug and outcome relationship to determine whether it shows causal traits. We taught a classifier to find patterns within these perspectives and it learned to discriminate between association and causality. The novelty of this research is the combination of supervised learning and Bradford Hill’s causality considerations to automate the Bradford Hill’s causality assessment.

We evaluated the framework on a drug safety gold standard known as the observational medical outcomes partnership’s non-specified association reference set. The methodology obtained excellent discrimination ability with area under the curves ranging between 0.792 and 0.940 (existing method optimal: 0.73) and a mean average precision of 0.640 (existing method optimal: 0.141). The proposed features can be calculated efficiently and be readily updated, making the framework suitable for big observational data.

Item Type: Article
Keywords: Big data; Pharmacovigilance; Longitudinal observational data; Causal effects; Signal detection
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine > Division of Epidemiology and Public Health
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: 10.1016/j.jbi.2015.06.011
Depositing User: Aickelin, Professor Uwe
Date Deposited: 14 Oct 2015 07:31
Last Modified: 18 Oct 2017 16:29
URI: http://eprints.nottingham.ac.uk/id/eprint/30450

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