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

TL;DR

PARHAF is a large, open-source, synthetic French clinical report corpus created by experts, enabling privacy-preserving NLP research and benchmarking in healthcare without sharing sensitive real data.

Contribution

It introduces a structured methodology for generating a comprehensive, shareable synthetic clinical corpus in French, covering multiple specialties and supporting NLP tasks.

Findings

Contains 7,394 reports for 5,009 patients across various specialties.

Supports multiple NLP use cases including information extraction and diagnostic coding.

Provides a privacy-preserving resource for training and evaluating clinical language models.

Abstract

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.