What are the machine learning techniques used in survival analysis?
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Machine learning techniques used in survival analysis include Cox-based deep neural network (DNN) models, random survival forest, Cox-based LASSO and Ridge models, autoencoder-based feature selection, and network-based approaches. The Cox-based DNN models, built with Keras and TensorFlow, have shown promising results in predicting survival outcomes . Random survival forest and Cox-based LASSO and Ridge models are also popular choices for survival prediction . Autoencoder-based feature selection methods have been proposed to reduce the number of features in high-dimensional survival data, improving prediction accuracy and speeding up the process . Network-based approaches, such as the DPWTE model, use neural networks to learn the distribution of event times and have shown performance improvements over existing models . These techniques provide versatile and accurate solutions for analyzing survival data.
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| Papers (5) | Insight |
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14 Sep 2021 3 Citations | The paper does not explicitly mention the machine learning techniques used in survival analysis. |
14 Sep 2020 4 Citations | The paper mentions that machine learning techniques such as gradient boosted trees, deep neural networks, and model-based boosting can be used in the context of time-to-event analysis. |
20 Apr 2021 | The paper mentions that machine learning algorithms, including random survival forest and Cox-based LASSO and Ridge models, have been widely applied for modeling high-dimensional survival data. |
28 May 2021 | The paper discusses the use of machine learning algorithms such as random survival forest and Cox-based LASSO and Ridge models in survival analysis. |
| The paper does not explicitly mention the machine learning techniques used in survival analysis. |
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