{"ID":2865839,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.20867","arxiv_id":"2509.20867","title":"Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments","abstract":"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.","short_abstract":"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 col...","url_abs":"https://arxiv.org/abs/2509.20867","url_pdf":"https://arxiv.org/pdf/2509.20867v1","authors":"[\"Christoph Düsing\",\"Philipp Cimiano\"]","published":"2025-09-25T08:00:05Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}