Using latent semantic indexing for information filtering
COCS '90 Proceedings of the ACM SIGOIS and IEEE CS TC-OA conference on Office information systems
Introduction to Information Retrieval
Introduction to Information Retrieval
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Methodological Review: Formal representation of eligibility criteria: A literature review
Journal of Biomedical Informatics
A practical method for transforming free-text eligibility criteria into computable criteria
Journal of Biomedical Informatics
Significant Cancer Prevention Factor Extraction: An Association Rule Discovery Approach
Journal of Medical Systems
Representing association classification rules mined from health data
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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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Objective: To identify Common Data Elements (CDEs) in eligibility criteria of multiple clinical trials studying the same disease using a human-computer collaborative approach. Design: A set of free-text eligibility criteria from clinical trials on two representative diseases, breast cancer and cardiovascular diseases, was sampled to identify disease-specific eligibility criteria CDEs. In this proposed approach, a semantic annotator is used to recognize Unified Medical Language Systems (UMLSs) terms within the eligibility criteria text. The Apriori algorithm is applied to mine frequent disease-specific UMLS terms, which are then filtered by a list of preferred UMLS semantic types, grouped by similarity based on the Dice coefficient, and, finally, manually reviewed. Measurements: Standard precision, recall, and F-score of the CDEs recommended by the proposed approach were measured with respect to manually identified CDEs. Results: Average precision and recall of the recommended CDEs for the two diseases were 0.823 and 0.797, respectively, leading to an average F-score of 0.810. In addition, the machine-powered CDEs covered 80% of the cardiovascular CDEs published by The American Heart Association and assigned by human experts. Conclusion: It is feasible and effort saving to use a human-computer collaborative approach to augment domain experts for identifying disease-specific CDEs from free-text clinical trial eligibility criteria.