Evaluation of a Natural Language Processing Model to Identify and Characterize Patients in the United States With High-Risk Non-Muscle-Invasive Bladder Cancer.

Vikram M NarayanDespina Siolas Eric S Meadows Vladimir TurzhitskyArthur SillahKentaro ImaiAndrew J McMurryHaojie Li

Published in: JCO clinical cancer informatics (2023)

The NLP model, combined with a rule-based algorithm, identified high-risk NMIBC with good performance and will enable future work to study real-world treatment patterns and clinical outcomes for high-risk NMIBC.

Keyphrases

muscle invasive bladder cancer
end stage renal disease
ejection fraction
newly diagnosed
chronic kidney disease
machine learning
autism spectrum disorder
deep learning
current status