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Neural network representation learning frameworks have recently shown to behighly effective at a wide range of tasks ranging from radiographyinterpretation via data-driven diagnostics to clinical decision support. Thisoften superior performance comes at the price of dramatically increasedtraining data requirements that cannot be satisfied in every given institutionor scenario. As a means of countering such data sparsity effects, distantsupervision alleviates the need for scarce in-domain data by relying on arelated, resource-rich, task for training.  This study presents an end-to-end neural clinical decision support systemthat recommends relevant literature for individual patients (few availableresources) via distant supervision on the well-known MIMIC-III collection(abundant resource). Our experiments show significant improvements in retrievaleffectiveness over traditional statistical as well as purely locally supervisedretrieval models.

Embedding Electronic Health Records for Clinical Information Retrieval