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CITATION.cff
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cff-version: 1.2.0
title: 'AnnoDash, a clinical terminology annotation dashboard'
message: Please cite AnnoDash using the metadata from this file.
type: software
authors:
- given-names: Justin
family-names: Xu
email: [email protected]
affiliation: The Hospital for Sick Children
orcid: 'https://orcid.org/0000-0003-4700-6277'
- given-names: Mjaye
family-names: Mazwi
email: [email protected]
affiliation: The Hospital for Sick Children
orcid: 'https://orcid.org/0000-0003-1345-5429'
- given-names: Alistair E W
family-names: Johnson
email: [email protected]
orcid: 'https://orcid.org/0000-0002-8735-3014'
affiliation: The Hospital for Sick Children
identifiers:
- type: doi
value: 10.5281/zenodo.8043943
description: 'AnnoDash, a clinical terminology annotation dashboard'
repository-code: 'https://github.com/justin13601/AnnoDash'
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).
keywords:
- clinical concepts
- ontology
- annotation
- natural language processing
- software
license: MIT