Paper Info
Title
Mining Electronic Medical Records to Explore the Linkage between Healthcare Resource Utilization and Disease Severity in Diabetic Patients
Abstract
Knowledge discovery in electronic health records (EHRs) is a central aspect for improved clinical decision making, prognosis, and patient management. While EHRs show great promise towards better data integration, automated access, and clinical workflow improvement, the vast information they capture over time pose challenges not only for medical practitioners, but also for the information analysis by machines. The objective of this paper is to promote and emphasize the importance of exploratory analytics that are commensurate with human capabilities and constraints. Within this realm we present a novel temporal event matrix representation and learning framework that discovers complex latent event patterns, which are easily interpretable by humans. We demonstrate our framework on synthetic data and on EHRs together with an extensive validation involving over 70,000 computed latent factor models. The present study is the first to link temporal patterns of healthcare resource utilization (HRU) against a diabetic disease complications severity index to better understand the relationships between disease severity and care delivery.
Year
DOI
Venue
2011
10.1109/HISB.2011.34
HISB
Keywords
Field
DocType
disease severity,mining electronic medical records,healthcare resource utilization,diabetic patients,clinical workflow improvement,complex latent event pattern,information analysis,better data integration,novel temporal event matrix,present study,computed latent factor model,improved clinical decision,diabetic disease complications severity,data mining,health care,knowledge discovery,diabetes,biomedical imaging,decision support systems,mathematical model,healthcare,history,data integration
Data integration,Data science,Health care,Disease,Computer science,Decision support system,Knowledge management,Medical record,Knowledge extraction,Analytics,Workflow
Conference
ISBN
Citations 
PageRank 
978-0-7695-4407-6
3
0.43
References 
Authors
0
6
Name
Order
Citations
PageRank
Noah Lee11287.70
Andrew F. Laine274783.01
Jianying Hu347835.52
Fei Wang42139135.03
Jimeng Sun54729240.91
Shahram Ebadollahi627523.21