MIMO: Mutual Integration of Patient Journey and Medical Ontology for
  Healthcare Representation Learning
  Healthcare representation learning on the Electronic Health Record (EHR) is
seen as crucial for predictive analytics in the medical field. Many natural
language processing techniques, such as word2vec, RNN and self-attention, have
been adapted for use in hierarchical and time stamped EHR data, but fail when
they lack either general or task-specific data. Hence, some recent works train
healthcare representations by incorporating medical ontology (a.k.a. knowledge
graph), by self-supervised tasks like diagnosis prediction, but (1) the
small-scale, monotonous ontology is insufficient for robust learning, and (2)
critical contexts or dependencies underlying patient journeys are never
exploited to enhance ontology learning. To address this, we propose an
end-to-end robust Transformer-based solution, Mutual Integration of patient
journey and Medical Ontology (MIMO) for healthcare representation learning and
predictive analytics. Specifically, it consists of task-specific representation
learning and graph-embedding modules to learn both patient journey and medical
ontology interactively. Consequently, this creates a mutual integration to
benefit both healthcare representation learning and medical ontology embedding.
Moreover, such integration is achieved by a joint training of both
task-specific predictive and ontology-based disease typing tasks based on fused
embeddings of the two modules. Experiments conducted on two real-world
diagnosis prediction datasets show that, our healthcare representation model
MIMO not only achieves better predictive results than previous state-of-the-art
approaches regardless of sufficient or insufficient training data, but also
derives more interpretable embeddings of diagnoses.
