Deep weighted survival neural networks to survival risk prediction
Abstract Survival risk prediction models have become important tools for clinicians to improve cancer treatment decisions. In the medical field, using gene expression data to build deep survival neural network models significantly improves accurate survival prognosis. However, it still poses a chall...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01670-2 |
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| _version_ | 1832571190024077312 |
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| author | Hui Yu Qingyong Wang Xiaobo Zhou Lichuan Gu Zihao Zhao |
| author_facet | Hui Yu Qingyong Wang Xiaobo Zhou Lichuan Gu Zihao Zhao |
| author_sort | Hui Yu |
| collection | DOAJ |
| description | Abstract Survival risk prediction models have become important tools for clinicians to improve cancer treatment decisions. In the medical field, using gene expression data to build deep survival neural network models significantly improves accurate survival prognosis. However, it still poses a challenge in building an efficient method to improve the accuracy of cancer-specific survival risk prediction, such as data noise problem. In order to solve the above problem, we propose a diversity reweighted deep survival neural network method with grid optimization (DRGONet) to improve the accuracy of cancer-specific survival risk prediction. Specifically, reweighting can be employed to adjust the weights assigned to each data point in the dataset based on their importance or relevance, thereby mitigating the impact of noisy or irrelevant data and improving model performance. Incorporating diversity into the goal of multiple learning models can help minimize bias and improve learning outcomes. Furthermore, hyperparameters can be optimized with grid optimization. Experimental results have demonstrated that our proposed approach has significant advantages (improved about 5%) in real-world medical scenarios, outperforming state-of-the-art comparison methods by a large margin. Our study highlights the significance of using DRGONet to overcome the limitations of building accurate survival prediction models. By implementing our technique in cancer research, we hope to reduce the suffering experienced by cancer patients and improve the effectiveness of treatment. |
| format | Article |
| id | doaj-art-ae7210bfef7f40c8b8bfee64e97a1eff |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-ae7210bfef7f40c8b8bfee64e97a1eff2025-02-02T12:49:36ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111111310.1007/s40747-024-01670-2Deep weighted survival neural networks to survival risk predictionHui Yu0Qingyong Wang1Xiaobo Zhou2Lichuan Gu3Zihao Zhao4School of Information and Artificial Intelligence, Anhui Agricultural UniversitySchool of Information and Artificial Intelligence, Anhui Agricultural UniversitySchool of Information and Artificial Intelligence, Anhui Agricultural UniversitySchool of Information and Artificial Intelligence, Anhui Agricultural UniversitySchool of Information and Artificial Intelligence, Anhui Agricultural UniversityAbstract Survival risk prediction models have become important tools for clinicians to improve cancer treatment decisions. In the medical field, using gene expression data to build deep survival neural network models significantly improves accurate survival prognosis. However, it still poses a challenge in building an efficient method to improve the accuracy of cancer-specific survival risk prediction, such as data noise problem. In order to solve the above problem, we propose a diversity reweighted deep survival neural network method with grid optimization (DRGONet) to improve the accuracy of cancer-specific survival risk prediction. Specifically, reweighting can be employed to adjust the weights assigned to each data point in the dataset based on their importance or relevance, thereby mitigating the impact of noisy or irrelevant data and improving model performance. Incorporating diversity into the goal of multiple learning models can help minimize bias and improve learning outcomes. Furthermore, hyperparameters can be optimized with grid optimization. Experimental results have demonstrated that our proposed approach has significant advantages (improved about 5%) in real-world medical scenarios, outperforming state-of-the-art comparison methods by a large margin. Our study highlights the significance of using DRGONet to overcome the limitations of building accurate survival prediction models. By implementing our technique in cancer research, we hope to reduce the suffering experienced by cancer patients and improve the effectiveness of treatment.https://doi.org/10.1007/s40747-024-01670-2Survival risk predictionSurvival neural networkDiversity reweightedGrid optimization |
| spellingShingle | Hui Yu Qingyong Wang Xiaobo Zhou Lichuan Gu Zihao Zhao Deep weighted survival neural networks to survival risk prediction Complex & Intelligent Systems Survival risk prediction Survival neural network Diversity reweighted Grid optimization |
| title | Deep weighted survival neural networks to survival risk prediction |
| title_full | Deep weighted survival neural networks to survival risk prediction |
| title_fullStr | Deep weighted survival neural networks to survival risk prediction |
| title_full_unstemmed | Deep weighted survival neural networks to survival risk prediction |
| title_short | Deep weighted survival neural networks to survival risk prediction |
| title_sort | deep weighted survival neural networks to survival risk prediction |
| topic | Survival risk prediction Survival neural network Diversity reweighted Grid optimization |
| url | https://doi.org/10.1007/s40747-024-01670-2 |
| work_keys_str_mv | AT huiyu deepweightedsurvivalneuralnetworkstosurvivalriskprediction AT qingyongwang deepweightedsurvivalneuralnetworkstosurvivalriskprediction AT xiaobozhou deepweightedsurvivalneuralnetworkstosurvivalriskprediction AT lichuangu deepweightedsurvivalneuralnetworkstosurvivalriskprediction AT zihaozhao deepweightedsurvivalneuralnetworkstosurvivalriskprediction |