J Am Med Inform Assoc - Predicting complications of percutaneous coronary intervention using a novel support vector method.

Tópicos

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Resumo

JECTIVE: To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI).MATERIALS AND METHODS: Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered.RESULTS: The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow (2) value (seven cases) and the mean cross-entropy error (eight cases).CONCLUSIONS: The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.

Resumo Limpo

jectiv explor feasibl novel approach use augment oneclass learn algorithm model inlaboratori complic percutan coronari intervent pcimateri method data blue cross blue shield michigan cardiovascular consortium bmc multicent registri year n use train model predict differ inlaboratori pci complic use novel oneplusclass support vector machin opsvm algorithm perform model term discrimin calibr compar perform model train use follow classif algorithm bmc data n logist regress lr oneclass support vector machin classif ocsvm twoclass support vector machin classif tcsvm opsvm tcsvm approach variant algorithm costsensit weight also consideredresult opsvm algorithm costsensit variant achiev highest area receiv oper characterist curv major pci complic studi eight case similar improv observ hosmerlemeshow valu seven case mean crossentropi error eight casesconclus opsvm algorithm base augment oneclass learn problem improv discrimin calibr across differ pci complic relat lr tradit support vector machin classif approach may valu broader rang clinic domain

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