TY - GEN
T1 - Predicting risk of complications following a drug eluting stent procedure
T2 - 2009 22nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2009
AU - Gouripeddi, Ramkiran
AU - Balasubramanian, Vineeth
AU - Panchanathan, Sethuraman
AU - Harris, Jenni
AU - Bhaskaran, Ambika
AU - Siegel, Robert M.
PY - 2009
Y1 - 2009
N2 - Drug Eluting Stents (DES) have distinct advantages over other Percutaneous Coronary Intervention procedures, but have recently been associated with the development of serious complications after the procedure. There is a growing need for understanding the risk of these complications, which has led to the development of simple statistical models. In this work, we have developed a predictive model based on Support Vector Machines on a real world live dataset consisting of clinical variables of patients being treated at a cardiac care facility to predict the risk of complications at 12 months following a DES procedure. A significant challenge in this work, common to most clinical machine learning datasets, was imbalanced data, and our results showed the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) to address this issue. The developed predictive model provided an accuracy of 94% with a 0.97 AUC (Area under ROC curve), indicating high potential to be used as a decision support for management of patients following a DES procedure in real-world cardiac care facilities.
AB - Drug Eluting Stents (DES) have distinct advantages over other Percutaneous Coronary Intervention procedures, but have recently been associated with the development of serious complications after the procedure. There is a growing need for understanding the risk of these complications, which has led to the development of simple statistical models. In this work, we have developed a predictive model based on Support Vector Machines on a real world live dataset consisting of clinical variables of patients being treated at a cardiac care facility to predict the risk of complications at 12 months following a DES procedure. A significant challenge in this work, common to most clinical machine learning datasets, was imbalanced data, and our results showed the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) to address this issue. The developed predictive model provided an accuracy of 94% with a 0.97 AUC (Area under ROC curve), indicating high potential to be used as a decision support for management of patients following a DES procedure in real-world cardiac care facilities.
UR - http://www.scopus.com/inward/record.url?scp=70449659517&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449659517&partnerID=8YFLogxK
U2 - 10.1109/CBMS.2009.5255454
DO - 10.1109/CBMS.2009.5255454
M3 - Conference contribution
AN - SCOPUS:70449659517
SN - 9781424448784
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
BT - 2009 22nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2009
Y2 - 2 August 2009 through 5 August 2009
ER -