Predictive Healthcare Financing Models Using Federated Learning on Multimodal Clinical and Claims Data
Keywords:
federated learning, predictive healthcare financing, multimodal analytics, electronic health recordsAbstract
Federated learning (FL) methods estimate healthcare finance using multimodal clinical and claims data. We want safe, privacy-preserving communication between hospitals, insurers, and payment systems without data aggregation. EHRs, imaging data, wearable device streams, and claims databases provide multimodal risk assessment, chronic care cost prediction, and funding distribution. Homomorphic encryption (HE) and differential privacy provide model efficacy and regulatory compliance. Analysis of cross-institutional federation model heterogeneity, communication efficiency, and convergence optimization. Federated prediction models improve chronic care resource allocation, financial risk, and sustainability. This research shows how to build secure, scalable, and interpretable healthcare ecosystem predictive financing systems.
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References
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