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

10/14/2019
by   Shaika Chowdhury, et al.
24

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.

READ FULL TEXT
research
01/01/2022

Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity

Multi-view learning attempts to generate a model with a better performan...
research
09/24/2020

BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes

Electronic health records (EHRs) are longitudinal records of a patient's...
research
03/11/2023

MetaViewer: Towards A Unified Multi-View Representation

Existing multi-view representation learning methods typically follow a s...
research
08/18/2017

Statistical Latent Space Approach for Mixed Data Modelling and Applications

The analysis of mixed data has been raising challenges in statistics and...
research
01/01/2023

Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning

In this paper, we study the problem of MOOC quality evaluation which is ...
research
07/20/2021

MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning

Healthcare representation learning on the Electronic Health Record (EHR)...
research
09/06/2018

Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis

Multi-omic data provides multiple views of the same patients. Integrativ...

Please sign up or login with your details

Forgot password? Click here to reset