EDEN: New Large-Scale Italian Clinical Notes Dataset Released for Medical AI Research
A team of researchers has published EDEN, a large-scale dataset of approximately 4 million anonymized clinical notes from Italian hospital emergency departments, along with a manually annotated subset of around 6,000 notes covering dyspnea and loss of consciousness cases. The corpus addresses a significant gap in non-English medical NLP resources and includes a structured annotation scheme developed by clinical experts using a 132-item Case Report Form. The dataset is intended to support the development and evaluation of Large Language Models for real-world Italian-language medical applications.
EDEN (Emergency Department Electronic Notes) is presented as the largest freely available corpus of clinical notes for the Italian language, comprising roughly 4 million fully anonymized notes collected from emergency departments across Italian hospitals. The notes span diverse phases of patient care during emergency department stays, providing broad coverage of clinical documentation practices. A curated subset of approximately 6,000 notes was manually annotated by multiple clinicians using a structured Case Report Form (CRF) containing 132 items relevant to two clinical scenarios: dyspnea and loss of consciousness. Annotation items span numerical, categorical, binary, and mixed value types, and the process underwent iterative revision to resolve ambiguities, though the resulting resource is noted to be highly imbalanced. The authors introduce CRF-filling as a novel structured information extraction benchmark and provide zero-shot baseline results using Gemma-27B and MedGemma-27B models. The dataset and methodology aim to close a critical gap in multilingual clinical NLP, where non-English resources have historically been scarce. The paper details the data collection protocol, on-site anonymization pipeline, corpus statistics, and annotation scheme.
What's missing
The paper acknowledges class imbalance in the annotated subset but does not detail mitigation strategies or their impact on downstream model performance. The geographic distribution of contributing hospitals is not specified, which may affect representativeness across Italian regional dialects and clinical practices. Inter-annotator agreement metrics are not prominently reported, leaving uncertainty about annotation reliability. Long-term data governance, access conditions, and update plans for the corpus are not described.
What different sources said
- arXiv cs.AICenter
EDEN: A Large-Scale Corpus of Clinical Notes for Italian
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