Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Representation Learning for Medical Data   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.

29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg