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<p>The proliferation of multimodal time-series healthcare data presents unprecedented opportunities for data-driven insights but also significant challenges due to pervasive missing values, especially in critical care environments. Traditional centralised imputation methods are often infeasible due to strict privacy regulations, while single-institution models suffer from poor generalizability. Federated learning offers a promising alternative but faces challenges, including statistical heterogeneity, temporal misalignment, and complex missingness patterns. This paper proposes Fed-HealthImp, a federated learning framework for deep imputation of missing values in multivariate, irregularly sampled clinical time-series. Our framework employs a self-attention-based imputation model with adaptive client weighting to handle non-IID data distributions across hospitals. We evaluate Fed-HealthImp on three real-world ICU datasets (eICU-CRD, MIMIC-IV, HiRID) under various missingness patterns. Results show that Fed-HealthImp achieves imputation quality within 3.5% of a privacy-violating centralised model, significantly outperforms local-only training, and improves downstream mortality prediction AUROC by up to 3.4%. Our work establishes a practical, privacy-preserving pathway for building robust imputation models from fragmented global ICU data.</p>

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A federated learning framework for deep imputation of missing data in heterogeneous ICU time series

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月18日

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  • Raja Vavekanand,
  • Anwar Ali Sathio,
  • Mujtaba Sultani

摘要

The proliferation of multimodal time-series healthcare data presents unprecedented opportunities for data-driven insights but also significant challenges due to pervasive missing values, especially in critical care environments. Traditional centralised imputation methods are often infeasible due to strict privacy regulations, while single-institution models suffer from poor generalizability. Federated learning offers a promising alternative but faces challenges, including statistical heterogeneity, temporal misalignment, and complex missingness patterns. This paper proposes Fed-HealthImp, a federated learning framework for deep imputation of missing values in multivariate, irregularly sampled clinical time-series. Our framework employs a self-attention-based imputation model with adaptive client weighting to handle non-IID data distributions across hospitals. We evaluate Fed-HealthImp on three real-world ICU datasets (eICU-CRD, MIMIC-IV, HiRID) under various missingness patterns. Results show that Fed-HealthImp achieves imputation quality within 3.5% of a privacy-violating centralised model, significantly outperforms local-only training, and improves downstream mortality prediction AUROC by up to 3.4%. Our work establishes a practical, privacy-preserving pathway for building robust imputation models from fragmented global ICU data.