Computer methods and programs in biomedicine
39368442
BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the wait...
As a favorable alternative to the censored quantile regression, censored expectile regression has been popular in survival analysis due to its flexibility in modeling the heterogeneous effect of covariates. The existing weighted expectile regression ...
Computer methods and programs in biomedicine
39342876
BACKGROUND AND OBJECTIVE: In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) ...
OBJECTIVE: Develop a time-dependent deep learning model to accurately predict the prognosis of pediatric glioma patients, which can assist clinicians in making precise treatment decisions and reducing patient risk.
BMC medical informatics and decision making
39334146
BACKGROUND: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemiological features, treatment principles, and prognostic factors remain controversial.
Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinica...
Computer methods and programs in biomedicine
39551025
BACKGROUND AND OBJECTIVES: The aim of this study is to develop a radiomic and deep learning-based signature for survival analysis of patients with Non-Small Cell Lung Cancer.
Prostate Cancer is a disease that affects the male reproductive system. The irregularity of the symptoms makes it hard for the clinicians to pinpoint the disease in the earlier stages. Techniques such as Machine Learning, Data Science, Deep Learning,...
The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning ...
An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure tim...