Federated generative models show promise for electronic health records

Researchers have developed federated generative event models (GEMs) for tokenized electronic health records, addressing data silos and performance degradation across different health systems. In an evaluation across three independent health systems, these federated GEMs demonstrated strong performance on clinical prediction tasks, approaching the effectiveness of centralized training. The study found that federated learning is technically feasible and preserves much of the centralized performance, offering significant benefits, especially when local training data is limited. AI

IMPACT Federated learning approaches for EHRs could unlock large, siloed datasets for improved clinical prediction and research.

RANK_REASON Academic paper detailing a new methodology for training AI models on sensitive data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Common Longitudinal ICU Data Format
• electronic health records
• FedAvg
• FedAvgM
• Federated generative event models for tokenized electronic health records
• Hugging Face
• LightGBM
• Michael C. Burkhart
• PR-AUC
• ROC-AUC

• paper
• infra

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