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10.1007/s13042-020-01155-x http://scihub22266oqcxt.onion/10.1007/s13042-020-01155-x C7308113!7308113 !33727983     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\33727983 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Int+J+Mach+Learn+Cybern 2020 ; 11 (12 ): 2849-2856 Nephropedia Template TP {{tp|p=33727983 |t=2020. A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction |pdf=|usr=}}{{33727983 }} gab.com Text A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction JO: Int J Mach Learn Cybern http://www.kidney.de/pget.php?&tw=1&pmid=33727983 #FOAvip Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status sequences, but it cannot represent complex interactions among different types of medical information, i.e., temporal medical event graphs, which can be represented by graph neural network (GNN). In this paper, we propose a hybrid method of RNN and GNN, called RGNN, for next-period prescription prediction from two views, where RNN is used to represent patient status sequences, and GNN is used to represent temporal medical event graphs. Experiments conducted on the public MIMIC-III ICU data show that the proposed method is effective for next-period prescription prediction, and RNN and GNN are mutually complementary. Twit Text FOAVip A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction ????Int J Mach Learn Cybern http://www.kidney.de/pget.php?&tw=1&pmid=33727983 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33727983 #FOAvip English Wikipedia <ref>{{Cite pmid|33727983 }}</ref> A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction #MMPMID33727983 Liu S ; Li T ; Ding H ; Tang B ; Wang X ; Chen Q ; Yan J ; Zhou Y Int J Mach Learn Cybern 2020[]; 11 (12 ): 2849-2856 PMID33727983 show ga Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status sequences, but it cannot represent complex interactions among different types of medical information, i.e., temporal medical event graphs, which can be represented by graph neural network (GNN). In this paper, we propose a hybrid method of RNN and GNN, called RGNN, for next-period prescription prediction from two views, where RNN is used to represent patient status sequences, and GNN is used to represent temporal medical event graphs. Experiments conducted on the public MIMIC-III ICU data show that the proposed method is effective for next-period prescription prediction, and RNN and GNN are mutually complementary. äA hybrid method of recurrent neural network and graph neural network f(epmc).pdf DeepDyve Pubget Overpricing