Conference Proceedings

Attributes for Causal Inference in Electronic Healthcare Databases

Jenna Reps, Jonathan M Garibaldi, Uwe Aickelin, Daniele Soria, Jack E Gibson, Richard B Hubbard, PP Rodrigues (ed.), M Pechenizkiy (ed.), J Gama (ed.), RC Correia (ed.), J Liu (ed.), A Traina (ed.), P Lucas (ed.), P Soda (ed.)

2013 IEEE 26TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) | IEEE | Published : 2013

Abstract

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria. © 2013 IEEE.

University of Melbourne Researchers