ArticleProceedings. IEEE International Conference on Healthcare Informatics2022
A comparison of few-shot and traditional named entity recognition models for medical text.
Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Prompt Framework for Extracting Scale-Related Knowledge Entities from Chinese Medical Literature: Development and Evaluation Study.Journal of medical Internet research · 2025Article
- Precision in Parsing: Evaluation of an Open-Source Named Entity Recognizer (NER) in Veterinary Oncology.Veterinary and comparative oncology · 2025Article
- Data Augmentation with Nearest Neighbor Classifier for Few-Shot Named Entity Recognition.Studies in health technology and informatics · 2024Article
- Few-shot learning for medical text: A review of advances, trends, and opportunities.Journal of biomedical informatics · 2023Review
- Artificial intelligence in dentistry-A review.Frontiers in dental medicine · 2023Review
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
Abstract
Many research problems involving medical texts have limited amounts of annotated data available (
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.