  Neural network representation learning frameworks have recently shown to be
highly effective at a wide range of tasks ranging from radiography
interpretation via data-driven diagnostics to clinical decision support. This
often superior performance comes at the price of dramatically increased
training data requirements that cannot be satisfied in every given institution
or scenario. As a means of countering such data sparsity effects, distant
supervision alleviates the need for scarce in-domain data by relying on a
related, resource-rich, task for training.
  This study presents an end-to-end neural clinical decision support system
that recommends relevant literature for individual patients (few available
resources) via distant supervision on the well-known MIMIC-III collection
(abundant resource). Our experiments show significant improvements in retrieval
effectiveness over traditional statistical as well as purely locally supervised
retrieval models.
