ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.JMIR medical informatics · 2024Article
- Processing of Short-Form Content in Clinical Narratives: Systematic Scoping Review.Journal of medical Internet research · 2024Article
- Clinical Note Structural Knowledge Improves Word Sense Disambiguation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Deciphering clinical abbreviations with a privacy protecting machine learning system.Nature communications · 2022Article
- Disambiguating Clinical Abbreviations Using a One-Fits-All Classifier Based on Deep Learning Techniques.Methods of information in medicine · 2022Article
- Automatically disambiguating medical acronyms with ontology-aware deep learning.Nature communications · 2021Article
- What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2021Article
- Zero-Shot Clinical Acronym Expansion via Latent Meaning Cells.Proceedings of machine learning research · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Article
- Making sense of abbreviations in nursing notes: A case study on mortality prediction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Interactive medical word sense disambiguation through informed learning.Journal of the American Medical Informatics Association : JAMIA · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Abbreviation disambiguation in clinical texts is a problem handled well by fully supervised machine learning methods. Acquiring training data, however, is expensive and would be impractical for large numbers of abbreviations in specialized corpora. An alternative is a semi-supervised approach, in which training data are automatically generated by substituting long forms in natural text with their corresponding abbreviations. Most prior implementations of this method either focus on very few abbreviations or do not test on real-world data. We present a realistic use case by testing several semi-supervised classification algorithms on a large hand-annotated medical record of occurrences of 74 ambiguous abbreviations. Despite notable differences between training and test corpora, classifiers achieve up to 90% accuracy. Our tests demonstrate that semi-supervised abbreviation disambiguation is a viable and extensible option for medical NLP systems.
Indexed as
Identifiers
28269852PMC5333249What 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.