Global partial orders from sequential data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Finding All Common Intervals of k Permutations
CPM '01 Proceedings of the 12th Annual Symposium on Combinatorial Pattern Matching
Redesigning electronic health record systems to support public health
Journal of Biomedical Informatics
Maximum Entropy Based Significance of Itemsets
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Journal of Computer and System Sciences
Medical coding classification by leveraging inter-code relationships
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 1st ACM International Health Informatics Symposium
Using PQ trees for comparative genomics
CPM'05 Proceedings of the 16th annual conference on Combinatorial Pattern Matching
Structural and temporal inference search (STIS): pattern identification in multimodal data
Proceedings of the 14th ACM international conference on Multimodal interaction
Mining high utility episodes in complex event sequences
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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The standardization and wider use of electronic medical records (EMR) creates opportunities for better understanding patterns of illness and care within and across medical systems. Our interest is in the temporal history of event codes embedded in patients' records, specifically investigating frequently occurring sequences of event codes across patients. In studying data from more than 1.6 million patient histories at the University of Michigan Health system we quickly realized that frequent sequences, while providing one level of data reduction, still constitute a serious analytical challenge as many involve alternate serializations of the same sets of codes. To further analyze these sequences, we designed an approach where a partial order is mined from frequent sequences of codes. We demonstrate an EMR mining system called EMRView that enables exploration of the precedence relationships to quickly identify and visualize partial order information encoded in key classes of patients. We demonstrate some important nuggets learned through our approach and also outline key challenges for future research based on our experiences.