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

Artificial Intelligence (122) Artificial Intelligence (1216) Clinical Informatics (919) Clinical Information and Decision Making (1340) Machine Learning (1401)

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

Authors of this article:

Kyeryoung Lee1 Author Orcid Image ;   Zongzhi Liu1 Author Orcid Image ;   Urmila Chandran2 Author Orcid Image ;   Iftekhar Kalsekar2 Author Orcid Image ;   Balaji Laxmanan2 Author Orcid Image ;   Mitchell K Higashi1 Author Orcid Image ;   Tomi Jun1 Author Orcid Image ;   Meng Ma1 Author Orcid Image ;   Minghao Li1 Author Orcid Image ;   Yun Mai1 Author Orcid Image ;   Christopher Gilman1 Author Orcid Image ;   Tongyu Wang1 Author Orcid Image ;   Lei Ai1 Author Orcid Image ;   Parag Aggarwal1 Author Orcid Image ;   Qi Pan1 Author Orcid Image ;   William Oh3 Author Orcid Image ;   Gustavo Stolovitzky1 Author Orcid Image ;   Eric Schadt3 Author Orcid Image ;   Xiaoyan Wang1 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Lee K, Lui Z, Mai Y, Higashi M, Jun T, Ma M, Wang T, Ai L, Calay E, Oh W, Stolovitzky G, Schadt E, Wang X. Empowering Clinical Trials with Natural Language Processing Models and Real-World Data: A Feasibility Study to Optimize Clinical Trial Eligibility Design with Data-driven Simulations (Preprint). JMIR AI 2023 View