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Lars Ericson

affiliation not provided to SSRN

  • affiliation not provided to SSRN

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A Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis

Number of pages: 26 Posted: 07 Mar 2022
Dongmei Sun, Thanh M. Nguyen, Robert J. Allaway, Jelai Wang, Verena Chung, Thomas V. Yu, Michael Mason, Isaac Dimitrovsky, Lars Ericson, Hongyang Li, Yuanfang Guan, Ariel Israel, Alex Olar, Balint Armin Pataki, RA2-DREAM Challenge Community, Gustavo Stolovitzky, Justin Guinney, Percio S. Gulko, Mason B. Frazier, James C. Costello, Jake Y. Chen and S. Louis Bridges
Cornell University - Hospital for Special Surgery, affiliation not provided to SSRN, Sage Bionetworks - Computational Oncology Group, affiliation not provided to SSRN, Sage Bionetworks - Computational Oncology Group, Sage Bionetworks, Sage Bionetworks, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, Sage Bionetworks, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, University of Alabama at Birmingham and Cornell University - Division of Rheumatology
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rheumatoid arthritis, machine learning, Quantify, radiographic, joint damage