Federated Multimodal Survival Modeling for Predicting Long-Term Patient Outcomes and Financial Burden
Keywords:
federated learning, multimodal data integration, survival analysis, electronic health records, genomic data, medical imaging, healthcare cost prediction, privacy-preserving machine learningAbstract
Diverse healthcare data enhances long-term patient outcomes and cost burden prediction but increases privacy, regulatory compliance, and institutional data silos. Federated multimodal survival modeling frameworks coordinate hospitals, laboratories, and payers without data aggregation. Electronic health data, genetic profiles, medical imaging features, and extensive billing histories are used to mimic time-to-event outcomes and cumulative cost trajectories under clinical and economic constraints. Federated learning improves discrete-time and deep Cox proportional hazards models using modality-specific encoders, cross-modal fusion, and safe parameter aggregation. Data heterogeneity, non-IID distributions, missing modalities, and institutional bias are addressed while retaining prediction validity and interpretability. We study privacy-preserving optimization, communication-efficient training, and cross-organizational deployment governance. Scalable and ethical, federated multimodal survival models predict long-term clinical outcomes and economic burden in complex healthcare ecosystems with performance equivalent to centralized baselines and decreased privacy risk.
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