ArticleScientific data2025
A textual dataset of de-identified health records in Spanish and Catalan for medical entity recognition and anonymization.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- An end-to-end system for explainable clinical coding across languages and diverse medical data sources.BMC medical informatics and decision making · 2026Article
- I-NOWJ: adaptive context selection and knowledge-guided learning for substance use named entity recognition.Database : the journal of biological databases and curation · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advancement of clinical natural language processing systems is crucial to exploit the wealth of textual data contained in medical records. Diverse data sources are required in different languages and from different sites to represent global health services. To this end, we have released CARMEN-I, a corpus of anonymized clinical records from the Hospital Clinic of Barcelona written during the COVID-19 pandemic spanning a period of two years. In addition to COVID-19 cases of adult patients, CARMEN-I features multiple comorbidities such as cardiovascular conditions, oncology treatments, post-transplant complications, and infectious diseases. This resource is publicly accessible together with detailed annotation guidelines and granular text-bound annotations generated in a collaborative effort between clinicians, linguists, and engineers to enable training and evaluation of automatic anonymization systems. Moreover, for information extraction purposes, a subset of 500 records is annotated with six relevant clinical concept classes: diseases, symptoms, procedures, medications, pathogens and humans.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.