Quinn Stanton Wells

According to our database, Quinn Stanton Wells authored at least 8 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics.

Megan M. Shuey, William W. Stead, Ida Aka, April L. Barnado, Lisa Bastarache, Elly Brokamp, Meredith Campbell, Robert J. Carroll, Jeffrey A. Goldstein, Adam Lewis, Beth A. Malow, Jonathan D. Mosley, Travis Osterman, Dolly A Padovani-Claudio, Andrea Ramirez, Dan M. Roden, Bryce A Schuler, Edward Siew, Jennifer Sucre, Isaac Thomsen, Rory J. Tinker, Sara Van Driest, Colin Walsh, Jeremy L. Warner, Quinn Stanton Wells, Lee E. Wheless

Bioinform., October, 2023

Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.

Nick Strayer, Siwei Zhang, Lydia Yao, Tess Vessels, Cosmin Adrian Bejan, Ryan S. Hsi, Jana K. Shirey-Rice, Justin M. Balko, Douglas B. Johnson, Elizabeth J. Phillips, Alex Bick, Todd L. Edwards, Digna R. Velez Edwards, Jill M. Pulley, Quinn Stanton Wells, Michael R. Savona, Nancy J. Cox, Dan M. Roden, Douglas M. Ruderfer, Yaomin Xu

Bioinform., January, 2023

2021

A retrospective approach to evaluating potential adverse outcomes associated with delay of procedures for cardiovascular and cancer-related diagnoses in the context of COVID-19.

Neil S. Zheng, Jeremy L. Warner, Travis J. Osterman, Quinn Stanton Wells, Xiao-Ou Shu, Stephen A. Deppen, Seth J. Karp, Shon Dwyer, QiPing Feng, Nancy J. Cox, Josh F. Peterson, C. Michael Stein, Dan M. Roden, Kevin B. Johnson, Wei-Qi Wei

J. Biomed. Informatics, 2021

Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort.

Sarah DeLozier, Sarah Bland, Melissa McPheeters, Quinn Stanton Wells, Eric Farber-Eger, Cosmin Adrian Bejan, Daniel Fabbri, S. Trent Rosenbloom, Dan M. Roden, Kevin B. Johnson, Wei-Qi Wei, Josh F. Peterson, Lisa Bastarache

J. Biomed. Informatics, 2021

Impact of Sex and Gender Disparities on Computational Phenotyping: A Potential Barrier to an Equitable Learning Health System.

Rebecca T. Levinson, Jennifer R. Malinowski, Luke V. Rasmussen, Suzette J. Bielinski, Véronique L. Roger, Quinn Stanton Wells, Laura K. Wiley

Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

2020

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.

Yucheng Tang, Riqiang Gao, Ho Hin Lee, Quinn Stanton Wells, Ashley Spann, James G. Terry, John Jeffrey Carr, Yuankai Huo, Shunxing Bao, Bennett A. Landman

Proceedings of the Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures, 2020

2019

Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study.

Juan Zhao, Yun Zhang, David J. Schlueter, Patrick Wu, Vern Eric Kerchberger, S. Trent Rosenbloom, Quinn Stanton Wells, QiPing Feng, Joshua C. Denny, Wei-Qi Wei

J. Biomed. Informatics, 2019

2014

Extraction of echocardiographic data from the electronic medical record is a rapid and efficient method for study of cardiac structure and function.

Quinn Stanton Wells, Eric Farber-Eger, Dana C. Crawford

J. Clin. Bioinform., 2014