Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments
This addresses the challenge of irregular temporal data in federated healthcare settings, offering a privacy-preserving solution for ICU data collaboration.
The paper tackled the problem of missing data in federated learning on electronic health records by proposing Federated Markov Imputation (FMI), a privacy-preserving method for temporal imputation in multi-centric ICU environments, and showed that it outperforms local imputation baselines on a sepsis onset prediction task using the MIMIC-IV dataset.
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.