Machine learning for survival analysis: A survey
Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …
where the outcome is the time until an event of interest occurs. One of the main challenges …
Sparse bayesian learning-based 3D radio environment map construction—Sampling optimization, scenario-dependent dictionary construction and sparse recovery
The radio environment map (REM), which can visualize the information of invisible
electromagnetic spectrum, is vital for monitoring, management, and security of spectrum …
electromagnetic spectrum, is vital for monitoring, management, and security of spectrum …
Generating hypergraph-based high-order representations of whole-slide histopathological images for survival prediction
Patient survival prediction based on gigapixel whole-slide histopathological images (WSIs)
has become increasingly prevalent in recent years. A key challenge of this task is achieving …
has become increasingly prevalent in recent years. A key challenge of this task is achieving …
Sparse bayesian learning-based hierarchical construction for 3D radio environment maps incorporating channel shadowing
The radio environment map (REM) visually displays the spectrum information over the
geographical map and plays a significant role in monitoring, management, and security of …
geographical map and plays a significant role in monitoring, management, and security of …
Assessing PD-L1 expression level by radiomic features from PET/CT in nonsmall cell lung cancer patients: an initial result
M Jiang, D Sun, Y Guo, Y Guo, J Xiao, L Wang… - Academic radiology, 2020 - Elsevier
Rationale and Objectives To explore the potential value of radiomic features-derived
approach in assessing PD-L1 expression status in nonsmall cell lung cancer (NSCLC) …
approach in assessing PD-L1 expression status in nonsmall cell lung cancer (NSCLC) …
An efficient sparse Bayesian learning algorithm based on Gaussian-scale mixtures
Sparse Bayesian learning (SBL) is a popular machine learning approach with a superior
generalization capability due to the sparsity of its adopted model. However, it entails a matrix …
generalization capability due to the sparsity of its adopted model. However, it entails a matrix …
Machine learning methods in organ transplantation
D Guijo-Rubio, PA Gutiérrez… - Current Opinion in …, 2020 - journals.lww.com
Organ transplantation can benefit from machine learning in such a way to improve the
current procedures for donor--recipient matching or to improve standard scores. However, a …
current procedures for donor--recipient matching or to improve standard scores. However, a …
Assessing EGFR gene mutation status in non-small cell lung cancer with imaging features from PET/CT
Objective The aim of this study was to investigate whether quantitative and qualitative
features extracted from PET/computed tomography (CT) can be used as imaging biomarkers …
features extracted from PET/computed tomography (CT) can be used as imaging biomarkers …
Forecasting crude oil price using EEMD and RVM with adaptive PSO-based kernels
Crude oil, as one of the most important energy sources in the world, plays a crucial role in
global economic events. An accurate prediction for crude oil price is an interesting and …
global economic events. An accurate prediction for crude oil price is an interesting and …
Bayesian inference of lymph node ratio estimation and survival prognosis for breast cancer patients
J Teng, A Abdygametova, J Du, B Ma… - IEEE Journal of …, 2019 - ieeexplore.ieee.org
Objective: We evaluated the prognostic value of lymph node ratio (LNR) for the survival of
breast cancer patients using Bayesian inference. Methods: Data on 5,279 women with …
breast cancer patients using Bayesian inference. Methods: Data on 5,279 women with …