An exclusive causal-leverage measure for detecting adverse drug reactions from electronic medical records

Abstract

Early detection of causal relationships between drugs and their associated adverse drug reactions (ADRs) can prevent harmful consequences or even deaths. Rare ADRs cannot be detected by pre-marketing clinical trials due to limitations in their size and duration. Existing postmarketing surveillance methods mainly rely on spontaneous reporting which is limited by severe underreporting (<10 percentage reporting rate), latency and inconsistency. In this paper, we propose to identify potential ADRs from electronic medical records which are accessible now in many hospitals. Specifically, we created a new interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model[1]. This measure extends our previous measure, called causal-leverage, and can more effectively reduce the effects of background noises in the data. On the basis of this new measure, a data mining algorithm was developed and tested on real patient data retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The retrieved data included 16,206 patients (15,605 male, 601 female). Experimental results showed that two known ADRs (i.e. hyperpotassemia and cough) associated with drug enalapril were ranked as 3 and 21, respectively, among all the 3,954 potential ADRs (ICD-9 codes) in our database.