EliXR: an approach to eligibility criteria extraction and representation
- Chunhua Weng1,
- Xiaoying Wu2,
- Zhihui Luo1,
- Mary Regina Boland1,
- Dimitri Theodoratos2,
- Stephen B Johnson1
- 1Department of Biomedical Informatics, Columbia University, New York, New York, USA
- 2Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA
- Correspondence to Chunhua Weng, Department of Biomedical Informatics, Columbia University, 622 W 168 Street, VC-5, New York, NY 10032, USA;
- Received 18 April 2011
- Accepted 22 June 2011
- Published Online First 31 July 2011
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 (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.
Conclusion 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.
- analytical processing
- clinical research eligibility criteria
- clinical research informatics
- collaborative technologies
- data exchange
- data integration
- data mining
- data models
- data warehousing
- knowledge acquisition
- knowledge acquisition and knowledge management
- knowledge representations
- linking the genotype and phenotype
- machine learning
- methods for integration of information from disparate sources
- natural-language processing
- predictive modelling
- privacy technology
- semantic network
- social/organizational study
- statistical learning
- text-based ontology learning
- visualization of data and knowledge
Funding This research was supported by the National Library of Medicine grants R01LM009886, R01LM010815, AHRQ grant R01 HS019853 and CTSA award UL1 RR024156.
Provenance and peer review Not commissioned; externally peer reviewed.