Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918098603343872 |
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| author | Kim, Jong-Min Ha, Il Do Kim, Sangjin |
| author_facet | Kim, Jong-Min Ha, Il Do Kim, Sangjin |
| contents | This research integrates deep learning, copula functions, and survival analysis to effectively handle highly correlated and right-censored multivariate survival data. It introduces copula-based activation functions (Clayton, Gumbel, and their combinations) to model the nonlinear dependencies inherent in such data. Through simulation studies and analysis of real breast cancer data, our proposed CNN-LSTM with copula-based activation functions for multivariate multi-types of survival responses enhances prediction accuracy by explicitly addressing right-censored data and capturing complex patterns. The model's performance is evaluated using Shewhart control charts, focusing on the average run length (ARL). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14641 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction Kim, Jong-Min Ha, Il Do Kim, Sangjin Machine Learning This research integrates deep learning, copula functions, and survival analysis to effectively handle highly correlated and right-censored multivariate survival data. It introduces copula-based activation functions (Clayton, Gumbel, and their combinations) to model the nonlinear dependencies inherent in such data. Through simulation studies and analysis of real breast cancer data, our proposed CNN-LSTM with copula-based activation functions for multivariate multi-types of survival responses enhances prediction accuracy by explicitly addressing right-censored data and capturing complex patterns. The model's performance is evaluated using Shewhart control charts, focusing on the average run length (ARL). |
| title | Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.14641 |