Personalized differential privacy budgets based on re-identification risk in federated learning produce lower error rates than fixed budgets on medical tabular data.
Deep learning for healthcare: review, opportunities and challenges
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CLIN-LLM combines uncertainty-calibrated BioBERT classification with retrieval-augmented FLAN-T5 generation and safety post-processing to reach 98% accuracy on clinical cases while cutting unsafe antibiotic suggestions by 67%.
citing papers explorer
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Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
Personalized differential privacy budgets based on re-identification risk in federated learning produce lower error rates than fixed budgets on medical tabular data.
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CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation
CLIN-LLM combines uncertainty-calibrated BioBERT classification with retrieval-augmented FLAN-T5 generation and safety post-processing to reach 98% accuracy on clinical cases while cutting unsafe antibiotic suggestions by 67%.