Journal of Biomedical Informatics
Journal of Biomedical Informatics
Methodological Review: Formal representation of eligibility criteria: A literature review
Journal of Biomedical Informatics
Text mining for efficient search and assisted creation of clinical trials
Proceedings of the ACM fifth international workshop on Data and text mining in biomedical informatics
Dynamic categorization of clinical research eligibility criteria by hierarchical clustering
Journal of Biomedical Informatics
Syntactic-Semantic frames for clinical cohort identification queries
DILS'12 Proceedings of the 8th international conference on Data Integration in the Life Sciences
Systematic identification of pharmacogenomics information from clinical trials
Journal of Biomedical Informatics
Inferring appropriate eligibility criteria in clinical trial protocols without labeled data
Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
Efficient management of multi-version clinical guidelines
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Dynamic multi-version ontology-based personalization
Proceedings of the Joint EDBT/ICDT 2013 Workshops
ADDIS: A decision support system for evidence-based medicine
Decision Support Systems
A semantic framework for intelligent matchmaking for clinical trial eligibility criteria
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
eTACTS: A method for dynamically filtering clinical trial search results
Journal of Biomedical Informatics
Unsupervised mining of frequent tags for clinical eligibility text indexing
Journal of Biomedical Informatics
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Formalizing eligibility criteria in a computer-interpretable language would facilitate eligibility determination for study subjects and the identification of studies on similar patient populations. Because such formalization is extremely labor intensive, we transform the problem from one of fully capturing the semantics of criteria directly in a formal expression language to one of annotating free-text criteria in a format called ERGO annotation. The annotation can be done manually, or it can be partially automated using natural-language processing techniques. We evaluated our approach in three ways. First, we assessed the extent to which ERGO annotations capture the semantics of 1000 eligibility criteria randomly drawn from ClinicalTrials.gov. Second, we demonstrated the practicality of the annotation process in a feasibility study. Finally, we demonstrate the computability of ERGO annotation by using it to (1) structure a library of eligibility criteria, (2) search for studies enrolling specified study populations, and (3) screen patients for potential eligibility for a study. We therefore demonstrate a new and practical method for incrementally capturing the semantics of free-text eligibility criteria into computable form.