• Corpus ID: 219636467

Information Extraction of Clinical Trial Eligibility Criteria

@article{Tseo2020InformationEO,
  title={Information Extraction of Clinical Trial Eligibility Criteria},
  author={Yitong Tseo and Markku I. Salkola and Ahmed Ahmed Mohamed and Anuj Kumar and Freddy Abnousi},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.07296}
}

• Yitong Tseo, M. Salkola, +2 authors F. Abnousi
• Published 12 June 2020
• Computer Science
• ArXiv

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 this https URL. 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.