title: EliXR: an approach to eligibility criteria extraction and representation.
abstract: OBJECTIVE
  To develop a semantic representation for clinical research eligibility criteria to automate semistructured information extraction from eligibility criteria text.
  
  
  METHODS
  An analysis pipeline called eligibility criteria extraction and representation (EliXR) was developed that integrates syntactic parsing and tree pattern mining to discover common semantic patterns in 1000 eligibility criteria randomly selected from http://ClinicalTrials.gov. The semantic patterns were aggregated and enriched with unified medical language systems semantic knowledge to form a semantic representation for clinical research eligibility criteria.
  
  
  RESULTS
  The authors arrived at 175 semantic patterns, which form 12 semantic role labels connected by their frequent semantic relations in a semantic network.
  
  
  RESULTS
  Three raters independently annotated all the sentence segments (N=396) for 79 test eligibility criteria using the 12 top-level semantic role labels. Eight-six per cent (339) of the sentence segments were unanimously labelled correctly and 13.8% (55) were correctly labelled by two raters. The Fleiss' κ was 0.88, indicating a nearly perfect interrater agreement.
  
  
  CONCLUSIONS
  This study present a semi-automated data-driven approach to developing a semantic network that aligns well with the top-level information structure in clinical research eligibility criteria text and demonstrates the feasibility of using the resulting semantic role labels to generate semistructured eligibility criteria with nearly perfect interrater reliability.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA. cw2384@columbia.edu
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Wu
authors.forename: Zhihui
authors.initials: Z
authors.lastname: Luo
authors.forename: Mary Regina
authors.initials: MR
authors.lastname: Boland
authors.forename: Dimitri
authors.initials: D
authors.lastname: Theodoratos
authors.forename: Stephen B
authors.initials: SB
authors.lastname: Johnson
mesh_terms: D000465:Algorithms; D035843:Biomedical Research; D057225:Data Mining; D004608:Eligibility Determination / Q000379:methods*; D009323:Natural Language Processing; D018579:Patient Selection; D012660:Semantics; D017432:Unified Medical Language System