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

Foundations of AI (47) Artificial Intelligence (1421) 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 (2) Tweetations (1) Metrics

Journals

  1. Lee K, Mai Y, Liu Z, Raja K, Jun T, Ma M, Wang T, Ai L, Calay E, Oh W, Schadt E, Wang X. CriteriaMapper: establishing the automatic identification of clinical trial cohorts from electronic health records by matching normalized eligibility criteria and patient clinical characteristics. Scientific Reports 2024;14(1) View
  2. Bikou A, Deligianni E, Dermiki-Gkana F, Liappas N, Teriús-Padrón J, Beltrán Jaunsarás M, Cabrera-Umpiérrez M, Kontogiorgis C. Improving Participant Recruitment in Clinical Trials: Comparative Analysis of Innovative Digital Platforms. Journal of Medical Internet Research 2024;26:e60504 View