Abstract

In this paper, we show how a text-based Recommendation Systems can greatly
benefit from neural statistical language models, more particularly BERT. We
evaluate the framework on a digital and collaborative platform for radiologists, by
automatically suggesting scientific papers from the medical database PubMed, to
provide evidence in diagnostic radiology. The models use contextualized vectors to
represent text, accounting for writing style, misspelling and jargon. By using precomputed representations of text passages, we are able to use compute-heavy
statistical language models in production environments, where supercomputers
are not available during inference.

The results suggests pre-computed embeddings are very effective when the texts
came from the same domain, and less effective (but still useful) in capturing the
interaction between clinical and scientific text. Nonetheless, the suggested
solutions hold promises in this and other areas in medicine. Possibly, the results
are transferable to other domains, such as processing of legal documents and
patent search.