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

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

Conference Proceedings

  1. Wang M, Ling J, Qiao P, Wang Y, Xiang Q, Zhu R, Hu Z. 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). Where Streams of Knowledge Converge: Constructing and Optimizing a Hierarchical Intelligent Question-Answering System for Agriculture View