Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915523050078208 |
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| author | Gao, Penglei Zou, Yan Duggal, Abhijit Huang, Shuaiqi Liang, Faming Wang, Xiaofeng |
| author_facet | Gao, Penglei Zou, Yan Duggal, Abhijit Huang, Shuaiqi Liang, Faming Wang, Xiaofeng |
| contents | We introduce the Functional Competing Risk Net (FCRN), a unified deep-learning framework for discrete-time survival analysis under competing risks, which seamlessly integrates functional covariates and handles missing data within an end-to-end model. By combining a micro-network Basis Layer for functional data representation with a gradient-based imputation module, FCRN simultaneously learns to impute missing values and predict event-specific hazards. Evaluated on multiple simulated datasets and a real-world ICU case study using the MIMIC-IV and Cleveland Clinic datasets, FCRN demonstrates substantial improvements in prediction accuracy over random survival forests and traditional competing risks models. This approach advances prognostic modeling in critical care by more effectively capturing dynamic risk factors and static predictors while accommodating irregular and incomplete data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation Gao, Penglei Zou, Yan Duggal, Abhijit Huang, Shuaiqi Liang, Faming Wang, Xiaofeng Machine Learning We introduce the Functional Competing Risk Net (FCRN), a unified deep-learning framework for discrete-time survival analysis under competing risks, which seamlessly integrates functional covariates and handles missing data within an end-to-end model. By combining a micro-network Basis Layer for functional data representation with a gradient-based imputation module, FCRN simultaneously learns to impute missing values and predict event-specific hazards. Evaluated on multiple simulated datasets and a real-world ICU case study using the MIMIC-IV and Cleveland Clinic datasets, FCRN demonstrates substantial improvements in prediction accuracy over random survival forests and traditional competing risks models. This approach advances prognostic modeling in critical care by more effectively capturing dynamic risk factors and static predictors while accommodating irregular and incomplete data. |
| title | Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.25381 |