Paper Info
Title
Experiences with mining temporal event sequences from electronic medical records: initial successes and some challenges
Abstract
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.
Year
DOI
Venue
2011
10.1145/2020408.2020468
KDD
Keywords
Field
DocType
electronic medical record,key class,medical system,key challenge,frequent sequence,michigan health system,initial success,partial order,data reduction,emr mining system,event code,temporal event sequence,medical informatics
Data science,Data mining,Computer science,Medical systems,Medical record,Health informatics,Temporal data mining,Standardization
Conference
Citations 
PageRank 
References 
29
1.07
16
Authors
6
Name
Order
Citations
PageRank
Debprakash Patnaik119114.89
Patrick Butler217311.71
Naren Ramakrishnan31913176.25
Laxmi Parida477377.21
Benjamin J. Keller523313.08
David A Hanauer619518.96