An Analysis of a BERT Deep Learning Strategy on a Technology Assisted Review Task

Document screening is a central task within EBM (Evidence-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 IR (Information Retrieval) tasks, I propose a DL document classification approach with BERT (Bidirectional Encoder Representations from Transformers) or PubMedBERT embeddings and a DL similarity search path using SBERT (Sentence-BERT) 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+RM3 (IR) 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