ArticleJAMIA open2023
AnnoDash, a clinical terminology annotation dashboard.
Article in JAMIA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Use of SNOMED CT in Large Language Models: Scoping Review.JMIR medical informatics · 2024Article
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
No grant is acknowledged in the PubMed record.
Abstract
Background: Standard ontologies are critical for interoperability and multisite analyses of health data. Nevertheless, mapping concepts to ontologies is often done with generic tools and is labor-intensive. Contextualizing candidate concepts within source data is also done in an ad hoc manner. Methods and Results: We present AnnoDash, a flexible dashboard to support annotation of concepts with terms from a given ontology. Text-based similarity is used to identify likely matches, and large language models are used to improve ontology ranking. A convenient interface is provided to visualize observations associated with a concept, supporting the disambiguation of vague concept descriptions. Time-series plots contrast the concept with known clinical measurements. We evaluated the dashboard qualitatively against several ontologies (SNOMED CT, LOINC, etc.) by using MIMIC-IV measurements. The dashboard is web-based and step-by-step instructions for deployment are provided, simplifying usage for nontechnical audiences. The modular code structure enables users to extend upon components, including improving similarity scoring, constructing new plots, or configuring new ontologies. Conclusion: AnnoDash, an improved clinical terminology annotation tool, can facilitate data harmonizing by promoting mapping of clinical data. AnnoDash is freely available at https://github.com/justin13601/AnnoDash (https://doi.org/10.5281/zenodo.8043943).
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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.