title: EliIE: An open-source information extraction system for clinical trial eligibility criteria.
abstract: OBJECTIVE
  To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0.
  
  
  METHODS
  EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring. Informaticians and domain experts were recruited to design an annotation guideline and generate a training corpus of annotated EC for 230 Alzheimer's clinical trials, which were represented as queries against the OMOP CDM and included 8008 entities, 3550 attributes, and 3529 relations. A sequence labeling-based method was developed for automatic entity and attribute recognition. Negation detection was supported by NegEx and a set of predefined rules. Relation extraction was achieved by a support vector machine classifier. We further performed terminology-based concept normalization and output structuring.
  
  
  RESULTS
  In task-specific evaluations, the best F1 score for entity recognition was 0.79, and for relation extraction was 0.89. The accuracy of negation detection was 0.94. The overall accuracy for query formalization was 0.71 in an end-to-end evaluation.
  
  
  CONCLUSIONS
  This study presents EliIE, an OMOP CDM-based information extraction system for automatic structuring and formalization of free-text EC. According to our evaluation, machine learning-based EliIE outperforms existing systems and shows promise to improve.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY, USA.
authors.forename: Tian
authors.initials: T
authors.lastname: Kang
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY, USA.
authors.forename: Shaodian
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Institute of Human Nutrition, Columbia University, New York, NY, USA.
authors.forename: Youlan
authors.initials: Y
authors.lastname: Tang
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY, USA.
authors.forename: Gregory W
authors.initials: GW
authors.lastname: Hruby
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Rusanov
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY, USA.
authors.forename: Noémie
authors.initials: N
authors.lastname: Elhadad
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY, USA.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
mesh_terms: D002986:Clinical Trials as Topic; D004608:Eligibility Determination / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D009323:Natural Language Processing; D018579:Patient Selection
keywords: clinical trials; common data model; machine learning; named entity recognition; natural language processing; patient selection