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dc.contributor.authorRavens, U.en_US
dc.contributor.authorKatircioglu-Öztürk, D.en_US
dc.contributor.authorWettwer, E.en_US
dc.contributor.authorChrist, T.en_US
dc.contributor.authorDobrev, D.en_US
dc.contributor.authorVoigt, N.en_US
dc.contributor.authorPoulet, C.en_US
dc.contributor.authorLoose, S.en_US
dc.contributor.authorSimon, J.en_US
dc.contributor.authorStein, A.en_US
dc.contributor.authorMatschke, K.en_US
dc.contributor.authorKnaut, M.en_US
dc.contributor.authorOto, E.en_US
dc.contributor.authorOto, A.en_US
dc.contributor.authorGüvenir, H. A.en_US
dc.date.accessioned2016-02-08T11:03:21Z
dc.date.available2016-02-08T11:03:21Z
dc.date.issued2015en_US
dc.identifier.issn0140-0118
dc.identifier.urihttp://hdl.handle.net/11693/26684
dc.description.abstractEx vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-and-dome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.en_US
dc.language.isoEnglishen_US
dc.source.titleMedical & Biological Engineering & Computingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s11517-014-1232-0en_US
dc.subjectHuman right atrial action potentialsen_US
dc.subjectRIMARC algorithmen_US
dc.subjectRisk predictionen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDiseasesen_US
dc.subjectElectrophysiologyen_US
dc.subjectForecastingen_US
dc.subjectLearning algorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectParameter estimationen_US
dc.subjectSocial aspectsen_US
dc.subjectStatistical testsen_US
dc.subjectArea under the ROC curveen_US
dc.subjectClinical parametersen_US
dc.subjectClinical settingsen_US
dc.subjectTriangular shapesen_US
dc.subjectClassification (of information)en_US
dc.subjectAdulten_US
dc.subjectAlgorithmen_US
dc.subjectAnthropometric parametersen_US
dc.subjectArea under the curveen_US
dc.subjectCardiovascular risken_US
dc.subjectClinical classificationen_US
dc.subjectClinical featureen_US
dc.subjectClinical studyen_US
dc.subjectDisease associationen_US
dc.subjectElectrophysiological proceduresen_US
dc.subjectEx vivo studyen_US
dc.subjectFemaleen_US
dc.subjectHealth statusen_US
dc.subjectHeart rhythmen_US
dc.subjectHemodynamic parametersen_US
dc.subjectHumanen_US
dc.subjectMachine learningen_US
dc.subjectMajor clinical studyen_US
dc.subjectMeasurement accuracyen_US
dc.subjectOpen heart surgeryen_US
dc.subjectPatient risken_US
dc.subjectPredictive valueen_US
dc.subjectPriority journalen_US
dc.subjectRanking instances by maximizing the area under the roc curveen_US
dc.subjectReceiver operating characteristicen_US
dc.subjectRetrospective studyen_US
dc.subjectRisk assessmenten_US
dc.subjectRisk factoren_US
dc.subjectAction potentialen_US
dc.subjectAtrial fibrillationen_US
dc.subjectHeart atriumen_US
dc.subjectPathophysiologyen_US
dc.subjectPhysiologyen_US
dc.subjectAgeden_US
dc.subjectMaleen_US
dc.subjectRisken_US
dc.subjectROC curveen_US
dc.titleApplication of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillationen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage263en_US
dc.citation.epage273en_US
dc.citation.volumeNumber53en_US
dc.citation.issueNumber3en_US
dc.identifier.doi10.1007/s11517-014-1232-0en_US
dc.publisherSpringeren_US
dc.identifier.eissn1741-0444


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