ArticleJournal of biomedical semantics2021
De-identifying Spanish medical texts - named entity recognition applied to radiology reports.
Article in Journal of biomedical semantics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature.Frontiers in digital health · 2021Pooled it
- A textual dataset of de-identified health records in Spanish and Catalan for medical entity recognition and anonymization.Scientific data · 2025Article
- Automated generation of discharge summaries: leveraging large language models with clinical data.Scientific reports · 2025Article
- A scoping review of large language model based approaches for information extraction from radiology reports.NPJ digital medicine · 2024Article
- Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models.BMC medical informatics and decision making · 2024Article
- Pragmatic De-Identification of Cross-Domain Unstructured Documents: A Utility-Preserving Approach with Relation Extraction Filtering.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish.JCO clinical cancer informatics · 2024Article
- Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing.Yearbook of medical informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedical texts such as radiology reports or electronic health records are a powerful source of data for researchers. Anonymization methods must be developed to de-identify documents containing personal information from both patients and medical staff. Although currently there are several anonymization strategies for the English language, they are also language-dependent. Here, we introduce a named entity recognition strategy for Spanish medical texts, translatable to other languages.
resultsWe tested 4 neural networks on our radiology reports dataset, achieving a recall of 97.18% of the identifying entities. Alongside, we developed a randomization algorithm to substitute the detected entities with new ones from the same category, making it virtually impossible to differentiate real data from synthetic data. The three best architectures were tested with the MEDDOCAN challenge dataset of electronic health records as an external test, achieving a recall of 69.18%.
conclusionsThe strategy proposed, combining named entity recognition tasks with randomization of entities, is suitable for Spanish radiology reports. It does not require a big training corpus, thus it could be easily extended to other languages and medical texts, such as electronic health records.
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.