  Concept normalization in free-form texts is a crucial step in every
text-mining pipeline. Neural architectures based on Bidirectional Encoder
Representations from Transformers (BERT) have achieved state-of-the-art results
in the biomedical domain. In the context of drug discovery and development,
clinical trials are necessary to establish the efficacy and safety of drugs. We
investigate the effectiveness of transferring concept normalization from the
general biomedical domain to the clinical trials domain in a zero-shot setting
with an absence of labeled data. We propose a simple and effective two-stage
neural approach based on fine-tuned BERT architectures. In the first stage, we
train a metric learning model that optimizes relative similarity of mentions
and concepts via triplet loss. The model is trained on available labeled
corpora of scientific abstracts to obtain vector embeddings of concept names
and entity mentions from texts. In the second stage, we find the closest
concept name representation in an embedding space to a given clinical mention.
We evaluated several models, including state-of-the-art architectures, on a
dataset of abstracts and a real-world dataset of trial records with
interventions and conditions mapped to drug and disease terminologies.
Extensive experiments validate the effectiveness of our approach in knowledge
transfer from the scientific literature to clinical trials.
