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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.20867 |
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| _version_ | 1866918147932553216 |
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| author | Düsing, Christoph Cimiano, Philipp |
| author_facet | Düsing, Christoph Cimiano, Philipp |
| contents | Missing data is a persistent challenge in federated learning on electronic health records, particularly when institutions collect time-series data at varying temporal granularities. To address this, we propose Federated Markov Imputation (FMI), a privacy-preserving method that enables Intensive Care Units (ICUs) to collaboratively build global transition models for temporal imputation. We evaluate FMI on a real-world sepsis onset prediction task using the MIMIC-IV dataset and show that it outperforms local imputation baselines, especially in scenarios with irregular sampling intervals across ICUs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20867 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments Düsing, Christoph Cimiano, Philipp Machine Learning Artificial Intelligence Missing data is a persistent challenge in federated learning on electronic health records, particularly when institutions collect time-series data at varying temporal granularities. To address this, we propose Federated Markov Imputation (FMI), a privacy-preserving method that enables Intensive Care Units (ICUs) to collaboratively build global transition models for temporal imputation. We evaluate FMI on a real-world sepsis onset prediction task using the MIMIC-IV dataset and show that it outperforms local imputation baselines, especially in scenarios with irregular sampling intervals across ICUs. |
| title | Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.20867 |