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Distributed representations have been used to support downstream tasks inhealthcare recently. Healthcare data (e.g., electronic health records) containmultiple modalities of data from heterogeneous sources that can providecomplementary information, alongside an added dimension to learningpersonalized patient representations. To this end, in this paper we propose anovel unsupervised encoder-decoder model, namely Mixed Pooling Multi-ViewAttention Autoencoder (MPVAA), that generates patient representationsencapsulating a holistic view of their medical profile. Specifically, by firstlearning personalized graph embeddings pertaining to each patient'sheterogeneous healthcare data, it then integrates the non-linear relationshipsamong them into a unified representation through multi-view attentionmechanism. Additionally, a mixed pooling strategy is incorporated in theencoding step to learn diverse information specific to each data modality.Experiments conducted for multiple tasks demonstrate the effectiveness of theproposed model over the state-of-the-art representation learning methods inhealthcare.

Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare