Methodological issues specific to prediction model development and evaluation
Y Jin, MW Kattan - Chest, 2023 - Elsevier
Developing and evaluating statistical prediction models is challenging, and many pitfalls can
arise. This article identifies what the authors feel are some common methodological …
arise. This article identifies what the authors feel are some common methodological …
Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study
Predicting outcomes in pulmonary tuberculosis is challenging despite effective treatments.
This study aimed to identify factors influencing treatment success and culture conversion …
This study aimed to identify factors influencing treatment success and culture conversion …
[HTML][HTML] Development and validation of a deep transfer learning-based multivariable survival model to predict overall survival in lung cancer
F Zhu, R Zhong, F Li, C Li, N Din… - Translational Lung …, 2023 - ncbi.nlm.nih.gov
Background Numerous deep learning-based survival models are being developed for
various diseases, but those that incorporate both deep learning and transfer learning are …
various diseases, but those that incorporate both deep learning and transfer learning are …
Utilizing Deep Learning and Computed Tomography to Determine Pulmonary Nodule Activity in Patients With Nontuberculous Mycobacterial-Lung Disease
AC Lancaster, ME Cardin, JA Nguyen… - Journal of Thoracic …, 2024 - journals.lww.com
Purpose: To develop and evaluate a deep convolutional neural network (DCNN) model for
the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung …
the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung …