ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019
Transfer Learning from BERT to Support Insertion of New Concepts into SNOMED CT.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Quantitatively assessing the impact of the quality of SNOMED CT subtype hierarchy on cohort queries.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Using language models and ontology topology to perform semantic mapping of traits between biomedical datasets.Bioinformatics (Oxford, England) · 2023Article
- A deep learning approach to identify missing is-a relations in SNOMED CT.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter.Nature communications · 2022Article
- Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study.JMIR medical informatics · 2021Article
- Automatic Structuring of Ontology Terms Based on Lexical Granularity and Machine Learning: Algorithm Development and Validation.JMIR medical informatics · 2020Article
- A review of auditing techniques for the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
With advances in Machine Learning (ML), neural network-based methods, such as Convolutional/Recurrent Neural Networks, have been proposed to assist terminology curators in the development and maintenance of terminologies. Bidirectional Encoder Representations from Transformers (BERT), a new language representation model, obtains state-of-the-art results on a wide array of general English NLP tasks. We explore BERT's applicability to medical terminology-related tasks. Utilizing the "next sentence prediction" capability of BERT, we show that the Fine-tuning strategy of Transfer Learning (TL) from the BERT
Indexed as
Identifiers
32308910PMC7153142What 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.