Predicting complications of percutaneous coronary intervention using a novel support vector method.
Objective: 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) multicen...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 20; no. 4; pp. 778 - 787 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104178397&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104178397 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jul2013 vid: 20 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104178397 NLM23599229 2012152091 10.1136/amiajnl-2012-001588 NLM23599229 PMC3721176 104178397 ppf: 778 ppct: 9 formats: tig: atl: Predicting complications of percutaneous coronary intervention using a novel support vector method. aug: au: Lee, Gyemin Gurm, Hitinder S Syed, Zeeshan affil: Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea. sug: subj: Models, Biological Algorithms Adverse Effects Pharmacokinetics Pilot Studies Human Logistic Regression ROC Curve Risk Assessment Methods ab: Objective: 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 χ(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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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