Med Biol Eng Comput - Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation.

Tópicos

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Resumo

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.

Resumo Limpo

ex vivo record action potenti ap human right atrial tissu patient sinus rhythm sr atrial fibril af display characterist spikeanddom triangular shape respect variabl huge within rhythm group aim studi appli machinelearn algorithm rank instanc maxim area roc curv rimarc larg data set ap combin retrospect collect general clinic paramet test whether rule learn rimarc algorithm can use accur classifi preoper rhythm status ap includ sr af patient learn phase rimarc algorithm establish rank order featur predict valu sr af model challeng addit test set featur patient rhythm status blind accuraci risk predict af model good featur use without seven ap featur accuraci still reach conclus shown train machinelearn algorithm rimarc experiment clinic data set allow predict classif test data set high accuraci clinic set approach may prove use find hypothesisgener associ differ paramet

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