Representation Learning for Medical Data

by   Karol Antczak, et al.

We propose a representation learning framework for medical diagnosis domain. It is based on heterogeneous network-based model of diagnostic data as well as modified metapath2vec algorithm for learning latent node representation. We compare the proposed algorithm with other representation learning methods in two practical case studies: symptom/disease classification and disease prediction. We observe a significant performance boost in these task resulting from learning representations of domain data in a form of heterogeneous network.


page 1

page 2

page 3

page 4


Representation Learning of EHR Data via Graph-Based Medical Entity Embedding

Automatic representation learning of key entities in electronic health r...

Representation Learning on Large and Small Data

Deep learning owes its success to three key factors: scale of data, enha...

Mittens: An Extension of GloVe for Learning Domain-Specialized Representations

We present a simple extension of the GloVe representation learning model...

HeteroMed: Heterogeneous Information Network for Medical Diagnosis

With the recent availability of Electronic Health Records (EHR) and grea...

Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation

This paper proposes a joint clinical natural language representation lea...

DeepQoE: A unified Framework for Learning to Predict Video QoE

Motivated by the prowess of deep learning (DL) based techniques in predi...

Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction

Multimodal neuroimage can provide complementary information about the de...