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A systematic review on natural language processing systems for eligibility prescreening in clinical research., , , and . J. Am. Medical Informatics Assoc., 29 (1): 197-206 (2021)The suitability of UMLS and SNOMED-CT for encoding outcome concepts., , , and . J. Am. Medical Informatics Assoc., 30 (12): 1895-1903 (November 2023)Cognitive Function Characterization Using Electronic Health Records Notes., , , , and . AMIA, AMIA, (2021)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)Improving Eligibility Prescreening for Alzheimer's Disease and Related Dementias Clinical Trials with Natural Language Processing. Columbia University, USA, (2022)Participatory Design of a Clinical Trial Eligibility Criteria Simplification Method., , , , , , , , , and . MIE, volume 281 of Studies in Health Technology and Informatics, page 984-988. IOS Press, (2021)Criteria2Query 2.0: Combining Machine Efficiency and Human Intelligence to Define a More Accurate and Feasible Cohort for Clinical Trial Recruitment., , , , , , and . AMIA, AMIA, (2022)Examining the Information Systems Success (ISS) of a mobile sexual health app (MyPEEPS Mobile) from the perspective of very young men who have sex with men (YMSM)., , , , , , , , , and 3 other author(s). Int. J. Medical Informatics, (2021)