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The settings used for the training model were as follows: (1) minibatch gradient descent with 1000 bench size for optimization; (2) learning rate=.05; (3) momentum coefficient=.9; (4) L2 regularization coefficient=.00001; and (5) tolerance of early stopping per 100 iterations=.0001. Multimedia Appendix 1 shows an example code for implementing the word embedding and CNNs for free-text discharge note classification.
Model architecture with 5 convolution channels and 1 full connection (FC) layer.
J Med Internet Res 2017;19(11):e380
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