Query-constraint-based association rule mining from diverse clinical datasets in the national sleep research resource
Rashmie Abeysinghe, Licong Cui · 2017
Secondary use of biomedical data has gained much attention recently to facilitate rapid knowledge discovery in biomedicine. Association Rule Mining (ARM) has been a popular technique for biomedical researchers to perform exploratory data analysis and discover potential relationships among variables in biomedical datasets. However, ARM of a high-dimensional biomedical dataset may produce a large number of rules that may not be interesting. In this paper, we introduce a query-constraint-based ARM (QARM) approach for exploratory analysis of diverse clinical datasets integrated in the National Sleep Research Resource (NSRR), which enables the rule mining on a subset of data containing items of interest based on a query constraint. In addition, biomedical datasets always contain semantically similar variables, thus we performed similar-variable-merging so that rules with simlar variables are not obtained. Applying QARM on five datasets from NSRR obtained a total of 6,921 rules with a minimum confidence of 60% (using top 50 rules for each query constraint).