Before revision:  We introduce a representation learning model based on word embeddings, convolutional neural networks  and autoencoders (i.e., "ConvAE" ) to transform patient trajectories into low-dimensional latent vectors.
After revision:  We introduce a representation learning model based on word embeddings, convolutional neural networks , and autoencoders (i.e., "ConvAE" ) to transform patient trajectories into low-dimensional latent vectors.