ArticleProceedings of machine learning research2020
Zero-Shot Clinical Acronym Expansion via Latent Meaning Cells.
Article in Proceedings of machine learning research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT.Scientific reports · 2026Article
- Processing of Short-Form Content in Clinical Narratives: Systematic Scoping Review.Journal of medical Internet research · 2024Article
- Disambiguation of acronyms in clinical narratives with large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study.JMIR medical informatics · 2024Article
- Exploring Large Language Models for Acronym, Symbol Sense Disambiguation, and Semantic Similarity and Relatedness Assessment.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Clinical Note Structural Knowledge Improves Word Sense Disambiguation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization.Proceedings of machine learning research · 2023Article
- Article
- 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
- 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
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
We introduce Latent Meaning Cells, a deep latent variable model which learns contextualized representations of words by combining local lexical context and metadata. Metadata can refer to granular context, such as section type, or to more global context, such as unique document ids. Reliance on metadata for contextualized representation learning is apropos in the clinical domain where text is semi-structured and expresses high variation in topics. We evaluate the LMC model on the task of zero-shot clinical acronym expansion across three datasets. The LMC significantly outperforms a diverse set of baselines at a fraction of the pre-training cost and learns clinically coherent representations. We demonstrate that not only is metadata itself very helpful for the task, but that the LMC inference algorithm provides an additional large benefit.
Indexed as
Identifiers
34790898PMC8594244What 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.