Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare
This work addresses the challenge of personalized patient representation learning in healthcare, which is incremental as it builds on existing representation learning methods with novel components.
The paper tackles the problem of learning patient representations from heterogeneous healthcare data by proposing MPVAA, an unsupervised encoder-decoder model that integrates multi-view attention and mixed pooling, achieving improved performance over state-of-the-art methods in multiple tasks.
Distributed representations have been used to support downstream tasks in healthcare recently. Healthcare data (e.g., electronic health records) contain multiple modalities of data from heterogeneous sources that can provide complementary information, alongside an added dimension to learning personalized patient representations. To this end, in this paper we propose a novel unsupervised encoder-decoder model, namely Mixed Pooling Multi-View Attention Autoencoder (MPVAA), that generates patient representations encapsulating a holistic view of their medical profile. Specifically, by first learning personalized graph embeddings pertaining to each patient's heterogeneous healthcare data, it then integrates the non-linear relationships among them into a unified representation through multi-view attention mechanism. Additionally, a mixed pooling strategy is incorporated in the encoding step to learn diverse information specific to each data modality. Experiments conducted for multiple tasks demonstrate the effectiveness of the proposed model over the state-of-the-art representation learning methods in healthcare.