# Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models

arXiv: https://arxiv.org/abs/1905.07002 · HTML: https://mlsanity.com/paper/1905.07002/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 20 of 20)

- [Large Language Models Can Be Strong Differentially Private Learners](https://mlsanity.com/paper/2110.05679/) (2021)
- [Harnessing large-language models to generate private synthetic text](https://mlsanity.com/paper/2306.01684/) (2023)
- [Selective Differential Privacy for Language Modeling](https://mlsanity.com/paper/2108.12944/) (2021)
- [Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes](https://mlsanity.com/paper/2309.00237/) (2023)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing](https://mlsanity.com/paper/2205.10095/) (2022)
- [Lightweight Transformers for Clinical Natural Language Processing](https://mlsanity.com/paper/2302.04725/) (2023)
- [Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting](https://mlsanity.com/paper/2305.12723/) (2023)
- [Generating Synthetic Text Data to Evaluate Causal Inference Methods](https://mlsanity.com/paper/2102.05638/) (2021)
- [Understanding the Effects of Domain Finetuning on LLMs](https://mlsanity.com/paper/2510.09359/) (2025)
- [Natural Language Generation in Healthcare: A Review of Methods and Applications](https://mlsanity.com/paper/2505.04073/) (2025)
- [Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records](https://mlsanity.com/paper/2403.08664/) (2024)
- [Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework](https://mlsanity.com/paper/2212.08349/) (2022)
- [Talking to Yourself: Defying Forgetting in Large Language Models](https://mlsanity.com/paper/2602.20162/) (2026)
- [PARHAF, a human-authored corpus of clinical reports for fictitious patients in French](https://mlsanity.com/paper/2603.20494/) (2026)
- [The NLP Sandbox: an efficient model-to-data system to enable federated and unbiased evaluation of clinical NLP models](https://mlsanity.com/paper/2206.14181/) (2022)
- [AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework](https://mlsanity.com/paper/2406.13947/) (2024)
- [Generating Completions for Broca's Aphasic Sentences Using Large Language Models](https://mlsanity.com/paper/2412.17669/) (2024)
- [Clinical Intent Extraction: A FHIR-Aligned Representation and the CIRCA Benchmark](https://mlsanity.com/paper/2609.29479/) (2026)
- [Synthetic Hospital: An Open, Verifiable, Physician-Validated Longitudinal EHR Benchmark](https://mlsanity.com/paper/2609.30027/) (2026)

## Abstract (arXiv)

Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemination of such data and give rise to suboptimal siloed research. De-identification methods attempt to address these concerns but were shown to be susceptible to adversarial attacks. In this work, we focus on the vast amounts of unstructured natural language data stored in clinical notes and propose to automatically generate synthetic clinical notes that are more amenable to sharing using generative models trained on real de-identified records. To evaluate the merit of such notes, we measure both their privacy preservation properties as well as utility in training clinical NLP models. Experiments using neural language models yield notes whose utility is close to that of the real ones in some clinical NLP tasks, yet leave ample room for future improvements.