TY - GEN
T1 - Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure
AU - Balasubramanian, Vineeth Nallure
AU - Gouripeddi, R.
AU - Panchanathan, Sethuraman
AU - Vermillion, J.
AU - Bhaskaran, A.
AU - Siegel, R. M.
PY - 2009
Y1 - 2009
N2 - Drug Eluting Stents (DES) have distinct advantages over other Percutaneous Coronary Intervention procedures, but have 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 statistical risk evaluation models. Conformal Predictors are a recently developed set of machine learning algorithms that allow not just risk classification on new patients, but add valid measures of confidence in predictions for individual patients. In this work, we have applied a novel Support Vector Machine (SVM) based conformal prediction framework to predict the risk of complications following a coronary DES procedure. This predictive model helps to risk stratify a patient for post-DES complications, and the valid measures of confidence can be used by the physician to make an informed, evidence-based decision to manage the patient appropriately.
AB - Drug Eluting Stents (DES) have distinct advantages over other Percutaneous Coronary Intervention procedures, but have 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 statistical risk evaluation models. Conformal Predictors are a recently developed set of machine learning algorithms that allow not just risk classification on new patients, but add valid measures of confidence in predictions for individual patients. In this work, we have applied a novel Support Vector Machine (SVM) based conformal prediction framework to predict the risk of complications following a coronary DES procedure. This predictive model helps to risk stratify a patient for post-DES complications, and the valid measures of confidence can be used by the physician to make an informed, evidence-based decision to manage the patient appropriately.
UR - http://www.scopus.com/inward/record.url?scp=77952697439&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77952697439&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77952697439
SN - 9781424472819
T3 - Computers in Cardiology
SP - 5
EP - 8
BT - Computers in Cardiology 2009, CinC 2009
T2 - 36th Annual Conference of Computers in Cardiology, CinC 2009
Y2 - 13 September 2009 through 16 September 2009
ER -