Improving Clinical Trial Cohort Definition Criteria and Enrollment with Distributional Semantic Matching

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Identifier 007_RR2016_Improving_Clinical_Trial_Cohort_SHAO.pdf
Title Improving Clinical Trial Cohort Definition Criteria and Enrollment with Distributional Semantic Matching
Creator Jianyin Shao; Ramkiran Gouripeddi; Julio C. Facelli, Department of Biomedical Informatics, University of Utah
Subject Evidence-Based Medicine; Research; Research Skills; Clinical Trials
Description Evidence-based medicine relies on well-designed/performed reproducible research. Clinical trials are the gold standard for evaluating clinical interventions on patients and populations. Current approaches for clinical trial cohort recruitment have multiple issues that have been extensively reported in the literature. Among these issues and relevant to the topic of this conference are (1) the ambiguities observed in eligibility criteria cohort definitions and variability in interpretation and queries made by research coordinators, which could be associated with the lack of reproducibility of certain clinical results; (2) challenges to enroll and retain expected number of participants, which could result in underpowered studies due to reduced number of participants. Using distributional semantic methods, we aim to automatically match extracted clinical concepts within clinical trial criteria and patient data. In our initial work we use a bag of concepts and a bag of negated concepts respectively to represent the clinical trial inclusion and exclusion criteria. Concept Bag algorithms are used to calculate a match score between the trial criteria and patient EHR data by measuring the similarity between the bags of concepts. We extracted clinical concepts using Metamap and tested our methods using a well-curated set of trials from clinicaltrials.gov and patient data. Results from this pilot study will inform the development of a trial criteria-patient matching framework as a service-oriented architecture that integrates with EHR systems and clinical workflows, engaging providers in the recruitment process at the point-of-care. Such an automated system will improve reproducibility of clinical research by (1) reducing the selection bias possibly introduced by trial investigators, and (2) increasing statistical power and reducing false discovery rate by facilitating enrollment of appropriate number of participants in a clinical research.
Relation is Part of 2016 Research Reproducibility Conference & Lectures
Publisher Spencer S. Eccles Health Sciences Library, University of Utah
Date Digital 2016
Date 2016
Format application/pdf
Rights Management Copyright 2016. For further information regarding the rights to this collection, please visit: https://NOVEL.utah.edu/about/copyright
Language eng
ARK ark:/87278/s6n05k61
Type Text
Setname ehsl_rr
Date Created 2019-02-21
Date Modified 2019-03-04
ID 1400678
Reference URL https://collections.lib.utah.edu/ark:/87278/s6n05k61