ArticleData in brief2026
Bangla-MedER: An annotated Bangla dataset for multi-type medical entity recognition from medical text.
Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical Entity Recognition (MedER) systems are needed to enhance the use and accessibility of Natural Language Processing (NLP) methods in the medical field. Since medical entity recognition in Bangla is a relatively new field, no such datasets are currently available in any repository. Unlike AI-generated data, which may contain biases or errors from auto- mated algorithms, the Bangla-MedER dataset is a manually curated resource for multi-type medical entity recognition in Bangla-language drug-indication text. A total of 2980 records were collected from publicly available medicine-related websites, pharmaceutical articles, and other sources of drug information. Each record contains the original Bangla medical text, along with expert-verified annotations across six entity types: medicine/chemical name, organ, disease, hormone, pharmacological class, and common medical terms. The raw transcribed text is provided to support reproducible research. All annotations were performed manually under the guidance of a certified medical expert, and proprietary brand names or personally identifiable information were removed to ensure privacy. The Bangla-MedER dataset enables a variety of applications, including medical named entity recognition in Bengali medical text, biomedical information retrieval, and clinical decision-support systems in a low-resource language environment. The complete raw dataset, along with documentation, is publicly available, offering a benchmark for medical entity recognition, healthcare NLP, biomedical informatics, and another related research.
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.