# PARHAF, a human-authored corpus of clinical reports for fictitious patients in French

arXiv: https://arxiv.org/abs/2603.20494 · HTML: https://mlsanity.com/paper/2603.20494/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [De-identification of Patient Notes with Recurrent Neural Networks](https://mlsanity.com/paper/1606.03475/) (2016)
- [Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models](https://mlsanity.com/paper/1905.07002/) (2019)
- [A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing](https://mlsanity.com/paper/2112.05780/) (2021)
- [FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection](https://mlsanity.com/paper/2309.10770/) (2023)
- [Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse](https://mlsanity.com/paper/2303.13451/) (2023)
- [PARROT: An Open Multilingual Radiology Reports Dataset](https://mlsanity.com/paper/2507.22939/) (2025)
- [Data-Constrained Synthesis of Training Data for De-Identification](https://mlsanity.com/paper/2502.14677/) (2025)

## Built on by (most cited first, 1 of 1)

- [Where Does the Signal Live? A Web Data Recipe for Medical Encoder Pretraining](https://mlsanity.com/paper/2606.22079/) (2026)

## Abstract (arXiv)

The development of clinical natural language processing (NLP) systems is severely hampered by the sensitive nature of medical records, which restricts data sharing under stringent privacy regulations, particularly in France and the broader European Union. To address this gap, we introduce PARHAF, a large open-source corpus of clinical documents in French. PARHAF comprises expert-authored clinical reports describing realistic yet entirely fictitious patient cases, making it anonymous and freely shareable by design. The corpus was developed using a structured protocol that combined clinician expertise with epidemiological guidance from the French National Health Data System (SNDS), ensuring broad clinical coverage. A total of 104 medical residents across 18 specialties authored and peer-reviewed the reports following predefined clinical scenarios and document templates. The corpus contains 7394 clinical reports covering 5009 patient cases across a wide range of medical and surgical specialties. It includes a general-purpose component designed to approximate real-world hospitalization distributions, and four specialized subsets that support information-extraction use cases in oncology, infectious diseases, and diagnostic coding. Documents are released under a CC-BY open license, with a portion temporarily embargoed to enable future benchmarking under controlled conditions. PARHAF provides a valuable resource for training and evaluating French clinical language models in a fully privacy-preserving setting, and establishes a replicable methodology for building shareable synthetic clinical corpora in other languages and health systems.