Automatic Detection Of Vaccine Adverse Reactions By Incorporating Historical Medical Conditions

Abstract

Identifying medical conditions that are correlated with vaccine adverse reactions can not only provide better understanding of how adverse reactions are triggered but also have the potential of detecting new adverse reactions that are otherwise hidden. We formulate this problem as mining frequent patterns with constraints. The major constraint we use is called the minimum dual-lift constraint, where dual-lift is a novel measure we propose to evaluate correlations in a pattern. We also introduce the notation of minimum improvement constraint to remove redundancy in generated pattern set. We come up with a novel approach to upper bound the dual-lift measure which helps to prune the search space. Experimental results show that our algorithm works significantly better than the baseline on dense datasets. Our algorithm is also tested on the real world VAERS database. Some interesting vaccine adverse reactions identified are presented.