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      Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation

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      Author(s)
      Ravens, U.
      Katircioglu-Öztürk, D.
      Wettwer, E.
      Christ, T.
      Dobrev, D.
      Voigt, N.
      Poulet, C.
      Loose, S.
      Simon, J.
      Stein, A.
      Matschke, K.
      Knaut, M.
      Oto, E.
      Oto, A.
      Güvenir, H. A.
      Date
      2015
      Source Title
      Medical & Biological Engineering & Computing
      Print ISSN
      0140-0118
      Electronic ISSN
      1741-0444
      Publisher
      Springer
      Volume
      53
      Issue
      3
      Pages
      263 - 273
      Language
      English
      Type
      Article
      Item Usage Stats
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      283
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      Abstract
      Ex 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.
      Keywords
      Human right atrial action potentials
      RIMARC algorithm
      Risk prediction
      Artificial intelligence
      Diseases
      Electrophysiology
      Forecasting
      Learning algorithms
      Learning systems
      Parameter estimation
      Social aspects
      Statistical tests
      Area under the ROC curve
      Clinical parameters
      Clinical settings
      Triangular shapes
      Classification (of information)
      Adult
      Algorithm
      Anthropometric parameters
      Area under the curve
      Cardiovascular risk
      Clinical classification
      Clinical feature
      Clinical study
      Disease association
      Electrophysiological procedures
      Ex vivo study
      Female
      Health status
      Heart rhythm
      Hemodynamic parameters
      Human
      Machine learning
      Major clinical study
      Measurement accuracy
      Open heart surgery
      Patient risk
      Predictive value
      Priority journal
      Ranking instances by maximizing the area under the roc curve
      Receiver operating characteristic
      Retrospective study
      Risk assessment
      Risk factor
      Action potential
      Atrial fibrillation
      Heart atrium
      Pathophysiology
      Physiology
      Aged
      Male
      Risk
      ROC curve
      Permalink
      http://hdl.handle.net/11693/26684
      Published Version (Please cite this version)
      http://dx.doi.org/10.1007/s11517-014-1232-0
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      • Department of Computer Engineering 1568
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