LDCT NER
An open source, high-performance information extraction NER model for LDCT reports.
Description: This model is an open-source Bi-LSTM NER model which can efficiently extract clinically relevant information from lung cancer screening CT reports with high accuracy, enabling efficient audit and feedback to improve quality of patient care.
Authors: Yucheng Zhang, Benjamin M. M. Grant, Andrew J. Hope, Rayjean J. Hung, Matthew T. Warkentin, Andrew CL Lam, Reenika Aggawal, Maria Xu, Frances A. Shepherd, Ming-Sound Tsao, Wei Xu, Mini Pakkal, Geoffrey Liu, Micheal C. McInnis
Lab: Xu
Keywords: NER, LDCT, information extraction
Implementation Language: Python
Licensing: Apache 2.0
Citation
Zhang Y, Grant BMM, Hope AJ, Hung RJ, Warkentin MT, Lam ACL, Aggawal R, Xu M, Shepherd FA, Tsao MS, Xu W, Pakkal M, Liu G, McInnis MC. Using Recurrent Neural Networks to Extract High-Quality Information From Lung Cancer Screening Computerized Tomography Reports for Inter-Radiologist Audit and Feedback Quality Improvement. JCO Clin Cancer Inform. 2023 Mar;7:e2200153.