document: Experiences with mining temporal event sequences from electronic medical records : initial successes and some challenges 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 .
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