This paper is in the following e-collection/theme issue:

Foundations of AI (47) Artificial Intelligence (1371) AI Data (13)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50800, first published .
Optimizing Clinical Trial Eligibility Design Using Natural Language Processing Models and Real-World Data: Algorithm Development and Validation

Optimizing Clinical Trial Eligibility Design Using Natural Language Processing Models and Real-World Data: Algorithm Development and Validation

Optimizing Clinical Trial Eligibility Design Using Natural Language Processing Models and Real-World Data: Algorithm Development and Validation

Authors of this article:

Kyeryoung Lee1 Author Orcid Image ;   Zongzhi Liu1 Author Orcid Image ;   Yun Mai1 Author Orcid Image ;   Tomi Jun1 Author Orcid Image ;   Meng Ma1 Author Orcid Image ;   Tongyu Wang1 Author Orcid Image ;   Lei Ai1 Author Orcid Image ;   Ediz Calay1 Author Orcid Image ;   William Oh1, 2 Author Orcid Image ;   Gustavo Stolovitzky1 Author Orcid Image ;   Eric Schadt1, 2 Author Orcid Image ;   Xiaoyan Wang1 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

Kyeryoung Lee   1 * , PhD ;   Zongzhi Liu   1 * , PhD ;   Yun Mai   1 , PhD ;   Tomi Jun   1 , MD ;   Meng Ma   1 , PhD ;   Tongyu Wang   1 , BSc ;   Lei Ai   1 , PhD ;   Ediz Calay   1 , PhD ;   William Oh   1, 2 , MD ;   Gustavo Stolovitzky   1 , PhD ;   Eric Schadt   1, 2 , PhD ;   Xiaoyan Wang   1 , PhD

1 GendDx (Sema4), Stamford, CT, United States

2 Icahn School of Medicine at Mount Sinai, New York, NY, United States

*these authors contributed equally

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