Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44537, first published .
Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning–Based Natural Language Processing

Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning–Based Natural Language Processing

Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning–Based Natural Language Processing

Journals

  1. Lee K, Liu Z, Mai Y, Jun T, Ma M, Wang T, Ai L, Calay E, Oh W, Stolovitzky G, Schadt E, Wang X. Optimizing Clinical Trial Eligibility Design Using Natural Language Processing Models and Real-World Data: Algorithm Development and Validation. JMIR AI 2024;3:e50800 View
  2. Wang L, Maolan A, Luo Y, Li Y, Liu R. Knowledge mapping analysis of ground glass nodules: a bibliometric analysis from 2013 to 2023. Frontiers in Oncology 2024;14 View