Variable Selection for Nonlinear Cox Regression Model via Deep Learning

Li, Kexuan

doi:10.5539/ijsp.v12n1p21

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…Moreover, existing scoring models are regression-based models and are limited by the number of covariates they can calculate. Consequently, the development of a scoring model utilizing numerous variables in EMR is currently being studied in deep learning 5 .…”

Section: Introductionmentioning

confidence: 99%

Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals

Kim,

Kang,

Seo

et al. 2024

Preprint

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Predicting major adverse cardiovascular events (MACE) is crucial due to its high readmission rate and severe sequelae. Current risk scoring model of MACE are based on a few features of a patient status at a single time point. We developed a self-attention-based model to predict MACE within 3 years from time series data utilizing numerous features in electronic medical records (EMRs). In addition, we demonstrated transfer learning for hospitals with insufficient data through code mapping and feature selection of top 50 features by the calculated importance. We established operational definitions and categories for diagnoses, medications, and laboratory tests to streamline scattered codes, enhancing clinical interpretability across hospitals. This resulted in reduced feature size and improved data quality for transfer learning. The pre-trained model demonstrated an increase in AUROC after transfer learning, from 0.564 to 0.821. Furthermore, to validate the effectiveness of the predicted scores, we analyzed the data using traditional survival analysis, which confirmed an elevated hazard ratio for a group of patients with high scores.

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Section: Introductionmentioning

confidence: 99%

Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals

Kim,

Kang,

Seo

et al. 2024

Preprint

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“…Another innovative method is Deep Feature Screening (DeepFS) [22], a non-parametric approach proposed by Li et al that overcomes the challenge of having high-dimensional and low-sample data. Li also applied deep learning to variable selection in nonlinear Cox regression models [23]. Chen et al proposed a graph convolutional network-based feature selection method called GRACES [24], which is specifically designed to handle high-dimensional and low-sample data and performs well on real-world datasets.…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Selection Method Based on Joint L2,1 Norm Minimization for Sparse Regression

Yang,

Zhu,

Liu

et al. 2023

Electronics

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Feature selection methods are widely used in machine learning tasks to reduce the dimensionality and improve the performance of the models. However, traditional feature selection methods based on regression often suffer from a lack of robustness and generalization ability and are easily affected by outliers in the data. To address this problem, we propose a robust feature selection method based on sparse regression. This method uses a non-square form of the L2,1 norm as both the loss function and regularization term, which can effectively enhance the model’s resistance to outliers and achieve feature selection simultaneously. Furthermore, to improve the model’s robustness and prevent overfitting, we add an elastic variable to the loss function. We design two efficient convergent iterative processes to solve the optimization problem based on the L2,1 norm and propose a robust joint sparse regression algorithm. Extensive experimental results on three public datasets show that our feature selection method outperforms other comparison methods.

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Deep feature screening: Feature selection for ultra high-dimensional data via deep neural networks

Wang

Liu

et al. 2023

Neurocomputing

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Variable Selection for Nonlinear Cox Regression Model via Deep Learning

Cited by 4 publications

References 33 publications

Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals

Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals

Robust Feature Selection Method Based on Joint L2,1 Norm Minimization for Sparse Regression

Deep feature screening: Feature selection for ultra high-dimensional data via deep neural networks

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