SOTAVerified

Representation Learning for Medical Data

2020-01-22Code Available0· sign in to hype

Karol Antczak

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

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.

Tasks

Reproductions