  Due to increased access to clinical trial outcomes and analysis, researchers
and scientists are able to iterate or improve upon relevant approaches more
effectively. However, the metrics and related results of clinical trials
typically do not follow any standardization in their reports, making it more
difficult for researchers to parse the results of different trials. The
objective of this paper is to describe an automated method utilizing natural
language processing in order to describe the probable core outcomes of clinical
trials, in order to alleviate the issues around disparate clinical trial
outcomes. As the nature of this process is domain specific, BioBERT was
employed in order to conduct a multi-class entity normalization task. In
addition to BioBERT, an unsupervised feature-based approach making use of only
the encoder output embedding representations for the outcomes and labels was
utilized. Finally, cosine similarity was calculated across the vectors to
obtain the semantic similarity. This method was able to both harness the
domain-specific context of each of the tokens from the learned embeddings of
the BioBERT model as well as a more stable metric of sentence similarity. Some
common outcomes identified using the Jaccard similarity in each of the
classifications were compiled, and while some are untenable, a pipeline for
which this automation process could be conducted was established.
