Developing a RadLex-Based Named Entity Recognition Tool for Mining Textual Radiology Reports: Development and Performance Evaluation Study

Journals

1. Zhou J, Guo X, Duan L, Yao Y, Shang Y, Wang Y, Xing B. Moving toward a standardized diagnostic statement of pituitary adenoma using an information extraction model: a real-world study based on electronic medical records. BMC Medical Informatics and Decision Making 2022;22(1) View
2. Lee K, Liu Z, Chandran U, Kalsekar I, Laxmanan B, Higashi M, Jun T, Ma M, Li M, Mai Y, Gilman C, Wang T, Ai L, Aggarwal P, Pan Q, Oh W, Stolovitzky G, Schadt E, Wang X. Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning–Based Natural Language Processing. JMIR AI 2023;2:e44537 View
3. Su Y, Babore Y, Kahn C. A Large Language Model to Detect Negated Expressions in Radiology Reports. Journal of Imaging Informatics in Medicine 2024 View
4. Chizhikova M, López-Úbeda P, Martín-Noguerol T, Díaz-Galiano M, Ureña-López L, Luna A, Martín-Valdivia M. Automatic TNM staging of colorectal cancer radiology reports using pre-trained language models. Computer Methods and Programs in Biomedicine 2024:108515 View