Privacy-Preserving Generation of Clinical Narratives from Medical Terminologies

arXiv:2509.10882 · cs.CL · Submitted 2025-09-13 · Read on arXiv

cs.CL

Submitted: 2025-09-13

Updated: 2026-08-30

Code: https://github.com/tatsu-lab/stanford

Terminology

• Fidelity — 25× in this paper · explained in 2 episodes
• Embeddings — 14× in this paper · explained in 2 episodes
• Differentially Private — 10× in this paper · explained in 3 episodes
• Differential Privacy — 8× in this paper · explained in 4 episodes
• Perplexity — 8× in this paper · explained in 3 episodes
• AUC — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Generative Model — 4× in this paper · explained in 2 episodes
• RAG — 4× in this paper · explained in 3 episodes
• Semantic Similarity — 4× in this paper · explained in 2 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Differential Privacy (DP) — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Membership Inference Attack (MIA) — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation — 1× in this paper
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Unified Medical Language System — 1× in this paper

Sources

• Scaling Laws for Neural Language Models
• Harnessing large-language models to generate private synthetic text
• BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
• Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
• Instruction Tuning with GPT-4
• Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead
• Qwen3 Technical Report
• Opacus: User-Friendly Differential Privacy Library in PyTorch

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