Attributes for causal inference in electronic healthcare databases

Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
2013 Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems  
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.
doi:10.1109/cbms.2013.6627871 dblp:conf/cbms/RepsGASGH13 fatcat:ajkvf6owx5hxxhmcwuxs2m3hpy