Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Relative risk and odds ratio: a data mining perspective
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining risk patterns in medical data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
On stability of a formal concept
Annals of Mathematics and Artificial Intelligence
Efficient discovery of risk patterns in medical data
Artificial Intelligence in Medicine
Non-redundant Subgroup Discovery Using a Closure System
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Domain Driven Data Mining
IEEE Transactions on Information Technology in Biomedicine
Review: Formal concept analysis in knowledge processing: A survey on applications
Expert Systems with Applications: An International Journal
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In this paper we discuss the problem of extracting and evaluating associations between drugs and adverse effects in pharma-covigilance data. Approaches proposed by the medical informatics community for mining one drug - one effect pairs perform an exhaustive search strategy that precludes from mining high-order associations. Some specificities of pharmacovigilance data prevent from applying pattern mining approaches proposed by the data mining community for similar problems dealing with epidemiological studies. We argue that Formal Concept Analysis (FCA) and concept lattices constitute a suitable framework for both identifying relevant associations, and assisting experts in their evaluation task. Demographic attributes are handled so that the disproportionality of an association is computed w.r.t. the relevant population stratum to prevent confounding. We put the focus on the understandability of the results and provide evaluation facilities for experts. A real case study on a subset of the French spontaneous reporting system shows that the method identifies known adverse drug reactions and some unknown associations that has to be further investigated.