Acknowledgements This work has been supported in part by the National Library of Medicine Grant# T15LM007124 and National Center for Advancing Translational Sciences of the National Institutes of Health Award# UL1TR001067. Contact Information Jianyin Shao jianyin.shao@utah.edu Flow of Information Discussion • On the 4x4 curated criteria sets, HCB with Dice yields the best performance with relatively high intra-set similarity and relatively low inter-set similarity. • HCB can provide similarities that match human expert cognition. • Pre-processing CUIs improves the similarity comparison, especially filtering out CUIs that are too common or general. • Other approaches can be used to refine concepts, such as using UMLS concept semantic types. • For matching patient records, metadata of the EHR can be used to select appropriate semantic types of the CUIs for similarity comparison. Introduction • Evidence-base medicine relies on well- designed and performed reproducible research. • Clinical trials are the gold standard of experimental design for examining effects of clinical intervention on patients or populations. • Current clinical trial cohort recruitment approaches may not promote reproducible research due to • Ambiguous cohort definition and varied interpretation by clinical trial coordinators. • Biases of selection of cases and controls. • Formal semantic matching patient medical record with appropriate trial eligibility criteria could improve the reproducibility of the clinical trials. Jianyin Shao, PhD1, Ramkiran Gouripeddi, MBBS, MS1,2, Julio C. Facelli, PhD1,2 Improving Clinical Trial Cohort Definition Criteria and Enrollment with Distributional Semantic Matching 1Department of Biomedical Informatics, 2Center for Clinical and Translational Science, University of Utah, Salt Lake City, Utah, USA Method • Concept Bag: clinical trial eligibility criteria and/or patient medical records can be represented by a set of UMLS concepts. • Hierarchical Concept Bag: trial criteria and medical records can be represented by a set of UMLS concepts and their ancestors. • Concepts extracted by MetaMap. • Similarity metrics: Jaccard, Dice, Cosine. Results References 1. Bradshaw RL. Concept Bag: A New Method for Computing Similarity. Ph. D. Dissertation, University of Utah; 2015. 2. Aronson a R. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp. 2001;17–21. 3. Wilcox A, Natarajan K, Weng C. Using Personal Health Records for Automated Clinical Trials Recruitment: the ePaIRing Model. Summit Transl Bioinforma. 2009;2009:136–40. Trial Criteria Extract concepts Filter out common CUIs Filter out improper POS Expand Hierarchical CUIs Pair-wise similarity for 4 sets of 4 trial eligibility criteria using different pre-processing and metrics All CUIs Retention of CUIs of specific POS* Removal of common CUIs** * Retained noun, verb, adjective, adverb and numerical cardinal CUIs. ** A common CUI list of 1648 CUIs was compiled by manual review of 195 randomly selected trial eligibility criteria (all trials ending with “000”) EHR documents or notes Extract concepts Filter out common CUIs Filter out improper POS Expand Hierarchical CUIs Match bags Score > threshold Prompt for trial notification Point of care Clinic Manager Trial Coordinator PHR