PREDICTION MODEL OF ATRIAL FIBRILLATION BASED ON CNN AND BIDIRECTIONAL LSTM
The existing model based on CNN cannot extract the temporal characteristics from patient data,while the models based on recurrent neural network ignore the different characteristics of various medical variables.To solve these problems,a predictive model of atrial fibrillation(AF)combined with CNN and RNN is proposed.This model used an independent CNN module to capture the different characteristics among medical variables in the electronic health records(EHR)data.At the same time,an independent RNN module was used to capture the temporal characteristics and correlation characteristics among medical variables in the EHR data.Experimental results on real hospital data sets show that compared with some of the latest disease prediction methods based on EHR data,the model performs better in predicting AF,with an increase of 2.14％in F1 and 1.32％in AUC.
Atrial fibrillationDisease predictionElectronic health recordsConvolutional neural networkLong short-term memory
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