  Document screening is a central task within Evidenced Based Medicine, which
is a clinical discipline that supplements scientific proof to back medical
decisions. Given the recent advances in DL (Deep Learning) methods applied to
Information Retrieval tasks, I propose a DL document classification approach
with BERT or PubMedBERT embeddings and a DL similarity search path using SBERT
embeddings to reduce physicians' tasks of screening and classifying immense
amounts of documents to answer clinical queries. I test and evaluate the
retrieval effectiveness of my DL strategy on the 2017 and 2018 CLEF eHealth
collections. I find that the proposed DL strategy works, I compare it to the
recently successful BM25 plus RM3 model, and conclude that the suggested method
accomplishes advanced retrieval performance in the initial ranking of the
articles with the aforementioned datasets, for the CLEF eHealth Technologically
Assisted Reviews in Empirical Medicine Task.
