luo-unsuperviseddrg-2010.pdf (299.49 kB)
Unsupervised DRG upcoding detection in healthcare databases
Diagnosis Related Group (DRG) upcoding is an anomaly in healthcare data that costs hundreds of millions of dollars in many developed countries. DRG upcoding is typically detected through resource intensive auditing. As supervised modeling of DRG upcoding is severely constrained by scope and timeliness of past audit data, we propose in this paper an unsupervised algorithm to filter data for potential identification of DRG upcoding. The algorithm has been applied to a hip replacement/revision dataset and a heart-attack dataset. The results are consistent with the assumptions held by domain experts.
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Event
International Conference on Data Mining Workshops (10th : 2010 : Sydney, N.S.W.)Pagination
600 - 605Publisher
IEEE Computer SocietyLocation
Sydney, New South WalesPlace of publication
Sydney, N.S.W.Start date
2010-12-14End date
2010-12-17ISBN-13
9780769542577Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2010, IEEEEditor/Contributor(s)
W Fan, W Hsu, G Webb, B Liu, C Zhang, D Gunopulos, X WuTitle of proceedings
ICDMW 2010 : Proceedings of 10th IEEE International Conference on Data Mining WorkshopsUsage metrics
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