The Joint Model of Longitudinal and Survival Data—Based on Machine Learning Methods

2015 ◽  
Vol 04 (04) ◽  
pp. 252-261
Author(s):  
征 温
Keyword(s):  
Machine Learning ◽  
Survival Data ◽  
Joint Model ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Longitudinal And Survival Data

scholarly journals A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Stefan Leger ◽  
Alex Zwanenburg ◽  
Karoline Pilz ◽  
Fabian Lohaus ◽  
Annett Linge ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Survival Data ◽  
Time To Event ◽  
Learning Methods ◽  
Risk Modelling ◽  
Machine Learning Methods

scholarly journals Sparse Boosting Based Machine Learning Methods for High-Dimensional Data

2021 ◽  
Author(s):  
Mu Yue
Keyword(s):  
Machine Learning ◽  
Parameter Estimation ◽  
Variable Selection ◽  
Survival Data ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Learning Methods ◽  
Require Time ◽  
Machine Learning Methods ◽  
Boosting Method

In high-dimensional data, penalized regression is often used for variable selection and parameter estimation. However, these methods typically require time-consuming cross-validation methods to select tuning parameters and retain more false positives under high dimensionality. This chapter discusses sparse boosting based machine learning methods in the following high-dimensional problems. First, a sparse boosting method to select important biomarkers is studied for the right censored survival data with high-dimensional biomarkers. Then, a two-step sparse boosting method to carry out the variable selection and the model-based prediction is studied for the high-dimensional longitudinal observations measured repeatedly over time. Finally, a multi-step sparse boosting method to identify patient subgroups that exhibit different treatment effects is studied for the high-dimensional dense longitudinal observations. This chapter intends to solve the problem of how to improve the accuracy and calculation speed of variable selection and parameter estimation in high-dimensional data. It aims to expand the application scope of sparse boosting and develop new methods of high-dimensional survival analysis, longitudinal data analysis, and subgroup analysis, which has great application prospects.

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