  Clinical trials predicate subject eligibility on a diversity of criteria
ranging from patient demographics to food allergies. Trials post their
requirements as semantically complex, unstructured free-text. Formalizing trial
criteria to a computer-interpretable syntax would facilitate eligibility
determination. In this paper, we investigate an information extraction (IE)
approach for grounding criteria from trials in ClinicalTrials(dot)gov to a
shared knowledge base. We frame the problem as a novel knowledge base
population task, and implement a solution combining machine learning and
context free grammar. To our knowledge, this work is the first criteria
extraction system to apply attention-based conditional random field
architecture for named entity recognition (NER), and word2vec embedding
clustering for named entity linking (NEL). We release the resources and core
components of our system on GitHub at
https://github.com/facebookresearch/Clinical-Trial-Parser. Finally, we report
our per module and end to end performances; we conclude that our system is
competitive with Criteria2Query, which we view as the current state-of-the-art
in criteria extraction.
