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Combining human and machine intelligence for clinical trial eligibility querying.

, , , , , , , , and . J. Am. Medical Informatics Assoc., 29 (7): 1161-1171 (2022)

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A data-driven approach to optimizing clinical study eligibility criteria., , , , , and . J. Biomed. Informatics, (June 2023)Sociotechnical feasibility of natural language processing-driven tools in clinical trial eligibility prescreening for Alzheimer's disease and related dementias., , , , , , , , , and 1 other author(s). J. Am. Medical Informatics Assoc., 31 (5): 1062-1073 (2024)Optimizing Clinical Research Eligibility Prescreening: An Iterative Usability Evaluation of an NLP-driven Cohort Identification Tool., , , , , and . AMIA, AMIA, (2022)Combining human and machine intelligence for clinical trial eligibility querying., , , , , , , , and . J. Am. Medical Informatics Assoc., 29 (7): 1161-1171 (2022)Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment., , , , , and . Int. J. Medical Informatics, (March 2023)Impact of IMPACT: Longitudinal Analysis of an Integrated Participant Scheduling System in a Clinical Research Setting., , , , , , , , , and . AMIA, AMIA, (2020)Cognitive Function Characterization Using Electronic Health Records Notes., , , , and . AMIA, AMIA, (2021)