Objective To develop a semantic representation for clinical research eligibility criteria to automate semistructured information extraction from eligibility criteria text. Materials and 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. Evaluation Three raters independently annotated all the sentence segments (N¼396) for 79 test eligibility criteria using the 12 top-level semantic role labels. Eightsix per cent (339) of the sentence segments were unanimously labelled correctly and 13.8% (55) were correctly labelled by two raters. The Fleiss' k was 0.88, indicating a nearly perfect interrater agreement. Conclusion This study present a semi-automated datadriven 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.
For example, EliXR provided a corpus-based knowledge acquisition framework that used the Unified Medical Language System (UMLS) to standardize eligibility-concept encoding and to enrich eligibility-concept relations for clinical research eligibility criteria from text REF .
EliXR: an approach to eligibility criteria extraction and representation
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association : JAMIA
Computer Science Medicine