  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.
