Predicting complications of percutaneous coronary intervention using a novel support vector method

Gyemin Lee, Hitinder S. Gurm, Zeeshan Syed

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Clinical tools to identify patients at risk of complications during percutaneous coronary intervention (PCI) are important to determine care at the bedside and to assess quality and outcomes. We address the growing need for such tools by proposing a novel support vector machine (SVM) approach to stratify PCI patients. Our approach simultaneously leverages properties of both one-class and two-class SVM classification to address the diminished prevalence of many important PCI complications. When studied on the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multi-center cardiology registry data, our SVM method provided moderate to high levels of discrimination for different PCI endpoints, and improved model performance in many cases relative to both traditional one-class and two-class SVMs.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE 2nd Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2012
Pages31
Number of pages1
DOIs
StatePublished - 2012
Event2012 IEEE 2nd Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2012 - San Diego, CA, United States
Duration: 27 Sep 201228 Sep 2012

Publication series

NameProceedings - 2012 IEEE 2nd Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2012

Conference

Conference2012 IEEE 2nd Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period27/09/1228/09/12

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