ArticleBMC medical informatics and decision making2021
A clinical trials corpus annotated with UMLS entities to enhance the access to evidence-based medicine.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers, 2 of them syntheses 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
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advances in orofacial pain research: a bibliometric analysis.Frontiers in neurology · 2025Pooled it
- Data extraction methods for systematic review (semi)automation: Update of a living systematic review.F1000Research · 2021Pooled it
- HybridNER: A Multi-Model Ensemble Framework for Robust Named Entity Recognition-From General Domains to Adversarial GNSS Scenarios.Sensors (Basel, Switzerland) · 2026Article
- Transformer-based relation extraction and concept normalization using an annotated clinical trials corpus.Scientific data · 2026Article
- Artificial Intelligence Meets Nail Diagnostics: Emerging Image-Based Sensing Platforms for Non-Invasive Disease Detection.Bioengineering (Basel, Switzerland) · 2026Review
- Cross- & multi-lingual medication detection: a transformer-based analysis.BMC medical informatics and decision making · 2025Article
- Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review.Findings of ACL. ACL · 2025Article
- Hierarchical embedding attention for overall survival prediction in lung cancer from unstructured EHRs.BMC medical informatics and decision making · 2025Article
- A clinical narrative corpus on nut allergy: annotation schema, guidelines and use case.Scientific data · 2025Article
- Hybrid natural language processing tool for semantic annotation of medical texts in Spanish.BMC bioinformatics · 2025Article
- The aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data.BMC medical informatics and decision making · 2024Article
- PheNormGPT: a framework for extraction and normalization of key medical findings.Database : the journal of biological databases and curation · 2024Article
- A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes.Database : the journal of biological databases and curation · 2024Article
- Explanatory argumentation in natural language for correct and incorrect medical diagnoses.Journal of biomedical semantics · 2024Article
- Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish.JCO clinical cancer informatics · 2024Article
- Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey.Yearbook of medical informatics · 2023Article
- Advances in monolingual and crosslingual automatic disability annotation in Spanish.BMC bioinformatics · 2023Article
- Constructing a disease database and using natural language processing to capture and standardize free text clinical information.Scientific reports · 2023Article
- NEREL-BIO: a dataset of biomedical abstracts annotated with nested named entities.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe large volume of medical literature makes it difficult for healthcare professionals to keep abreast of the latest studies that support Evidence-Based Medicine. Natural language processing enhances the access to relevant information, and gold standard corpora are required to improve systems. To contribute with a new dataset for this domain, we collected the Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) corpus.
methodsWe annotated 1200 texts about clinical trials with entities from the Unified Medical Language System semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM), pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC). We doubly annotated 10% of the corpus and measured inter-annotator agreement (IAA) using F-measure. As use case, we run medical entity recognition experiments with neural network models.
resultsThis resource contains 500 abstracts of journal articles about clinical trials and 700 announcements of trial protocols (292 173 tokens). We annotated 46 699 entities (13.98% are nested entities). Regarding IAA agreement, we obtained an average F-measure of 85.65% (±4.79, strict match) and 93.94% (±3.31, relaxed match). In the use case experiments, we achieved recognition results ranging from 80.28% (±00.99) to 86.74% (±00.19) of average F-measure.
conclusionsOur results show that this resource is adequate for experiments with state-of-the-art approaches to biomedical named entity recognition. It is freely distributed at: http://www.lllf.uam.es/ESP/nlpmedterm_en.html . The methods are generalizable to other languages with similar available sources.
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.