Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
PlanMine: Predicting Plan Failures Using Sequence Mining
Artificial Intelligence Review - Issues on the application of data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Temporal Patterns Without Predefined Time Windows
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
An event set approach to sequence discovery in medical data
Intelligent Data Analysis
Finding event-oriented patterns in long temporal sequences
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining Unexpected Temporal Associations: Applications in Detecting Adverse Drug Reactions
IEEE Transactions on Information Technology in Biomedicine
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It is useful, sometimes crucial in medicine domain, to discover a temporal association or causal relationship among events. Such a mining problem is often challenging because 'consequence events' may not reliably occur after each trigger event of interest. This makes it difficult to apply existing temporal data mining techniques directly to real world problems. In this paper, we formalise the problem of mining consequence events of newly-introduced interventions. We combine the Before-After-Control-Impact (BACI) design with frequent pattern mining techniques to define an interestingness measure called consequency. We then propose a Multiple Occurrence of Target events Mining (MOTM) algorithm. MOTM is applied to the real world problem of monitoring the consequence effects of newly-marketed medicines in linked administrative health databases. The results for the case of the cholesterol lowering drug atorvastatin highlight the consequence events with lowest negative consequency values, which suggest replacement of existing therapies with the new one. The consequence events with highest consequency values are likely to be associated with adverse reactions of atorvastatin or treatments of cardiovascular (or associated) conditions. Sensitivity examination of MOTM on another drug further illustrates its effectiveness.