EDEN: A Large-Scale Corpus of Clinical Notes for Italian
Quick Answer
EDEN is the largest freely available corpus of clinical notes in Italian, comprising 4 million anonymized notes from emergency departments.
Quick Take
EDEN is the largest freely available corpus of clinical notes in Italian, comprising 4 million anonymized notes from emergency departments. It includes 6,000 manually annotated notes for dyspnea and loss of consciousness, supporting the development of Large Language Models in medical applications. The dataset introduces a novel CRF-filling benchmark with zero-shot baselines from Gemma-27B and MedGemma-27B.
Key Points
- EDEN corpus contains approximately 4 million anonymized clinical notes.
- 6,000 notes are manually annotated by clinical experts using a structured Case Report Form.
- The dataset addresses a significant gap in Italian clinical data for language model training.
- CRF-filling is proposed as a new benchmark for structured information extraction.
- Zero-shot baselines achieved using Gemma-27B and MedGemma-27B models.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 12569v1 Announce Type: new Abstract: We present EDEN (Emergency Department Electronic Notes), a new and unique large-scale corpus of clinical notes produced in Emergency Departments of Italian hospitals. The corpus, in its current version, is composed of approximately 4 million clinical notes fully anonymized, covering diverse phases of patient care during the stay in the emergency department.
In addition, a subset of about six thousand notes has been manually annotated by clinical experts through a structured Case Report Form (CRF) containing 132 items relevant for two patient situations in emergency departments, dyspnea and loss of consciousness. Items may assume numerical values (e. g. , for blood saturation), categorical (e. g. , for level of consciousness ), binary (e. g. , for presence of traumas), and mixed value types.
The annotation process involved multiple clinicians and underwent iterative revision to resolve ambiguities in item formulation, resulting in a richly structured (although high imbalanced) resource. The dataset aims to fill a relevant gap of data able to support both the development and the use of Large Language Models in concrete medical applications. We describe the data collection protocol, the on-site anonymisation pipeline, corpus statistics, and the annotation scheme.
Finally, we propose CRF-filling as a novel structured information extraction benchmark, and provide zero-shot baseline resulting from Gemma-27B and MedGemma-27B. To the best of our knowledge, the EDEN dataset is the largest freely available corpus of clinical notes existing for the Italian language.
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