| アブストラクト | Electronic Health Records (EHRs) provide rich opportunities for developing risk prediction tools to support clinical decision-making, yet they are inherently incomplete because data are recorded selectively during routine care. Such missingness may be informative, reflecting clinical judgment and patient status, and missing data patterns can shift between model development and real-world deployment. These challenges limit the reliability and transportability of predictive models in healthcare settings. We propose an imputation-free framework that jointly trains Conditional Variational Autoencoders with deep survival models to enable risk prediction directly from incomplete EHR data. We demonstrate the approach using the deep survival model DeSurv and evaluate its performance through simulation studies and two retrospective cohorts from the Clinical Practice Research Datalink primary care database. The proposed framework consistently outperforms conventional missing data methods, achieving superior performance on ground-truth metrics in simulations and improved calibration-based survival metrics in real-world cohorts. It also demonstrates increased robustness to unseen missingness patterns and distributional shifts. By providing a unified strategy for handling missing data across development, validation, and deployment, this work advances methodological robustness in healthcare informatics and supports more reliable clinical risk prediction in practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s41666-026-00234-y. |
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| ジャーナル名 | Journal of healthcare informatics research |
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| Pubmed追加日 | 2026/5/11 |
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| 投稿者 | Hong, Natalia; Acharya, Aditya; Gokhale, Krishna; Cooper, Jenny; Gadd, Charles; Crowe, Francesca; Nirantharakumar, Krishnarajah; Yau, Christopher |
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| 組織名 | Nuffield Department of Women's & Reproductive Health, University of Oxford,;Oxford, United Kingdom. ROR: https://ror.org/052gg0110. GRID: grid.4991.5. ISNI:;0000 0004 1936 8948;Health Data Research UK, London, United Kingdom. ROR: https://ror.org/04rtjaj74.;GRID: grid.507332.0. ISNI: 0000 0004 9548 940X;The Alan Turing Institute, London, United Kingdom. ROR:;https://ror.org/035dkdb55. GRID: grid.499548.d. ISNI: 0000 0004 5903 3632;Department of Applied Health Sciences, University of Birmingham, Birmingham,;United Kingdom. ROR: https://ror.org/03angcq70. GRID: grid.6572.6. ISNI: 0000;0004 1936 7486;Department of Population Health Sciences, Kings College London, London, United;Kingdom. ROR: https://ror.org/0220mzb33. GRID: grid.13097.3c. ISNI: 0000 0001;2322 6764 |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/42110656/ |
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