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We have two main contributions in this work: 1. We explore the usage of astacked denoising autoencoder, and a paragraph vector model to learntask-independent dense patient representations directly from clinical notes. Weevaluate these representations by using them as features in multiple supervisedsetups, and compare their performance with those of sparse representations. 2.To understand and interpret the representations, we explore the best encodedfeatures within the patient representations obtained from the autoencodermodel. Further, we calculate the significance of the input features of thetrained classifiers when we use these pretrained representations as input.

Unsupervised patient representations from clinical notes with interpretable classification decisions