Representation Learning for Medical Data
This work addresses medical diagnosis challenges for healthcare applications, but it appears incremental as it builds on existing representation learning methods.
The authors tackled the problem of medical diagnosis by proposing a representation learning framework based on a heterogeneous network model and a modified metapath2vec algorithm, resulting in a significant performance boost in symptom/disease classification and disease prediction tasks.
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.