Assessment of contextualised representations in detecting outcome phrases in clinical trials.
Author(s): Micheal Abaho*, Danushka Bollegala, Paula Williamson and Susanna Dodd
Background: Automating the recognition of out- comes reported in clinical trials using machine learning has a huge potential of speeding up access to evidence necessary in healthcare decision making. Prior research has however acknowledged inadequate training corpora as a challenge for the Outcome detection (OD) task. Additionally, several contextualised representations (embeddings) like BERT and ELMO have achieved unparalleled success in detecting various diseases, genes, proteins and chemicals, however, the same cannot be emphatically stated for outcomes, because these representation models have been relatively undertested and studied for the OD task. Methods: We introduce “EBM-COMET”, a dataset in which 300 Randomised Clinical Trial (RCT) PubMed abstracts are expertly annotated for clinical outcomes. Unlike prior related datasets that use arbitrary outcome class.. Read More»