Annodash - A Clinical Terminology Annotation Dashboard | The Hospital for Sick Children Research Symposium

Standard ontologies are critical for interoperability and multi-site 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. We present AnnoDash (formerly called MIMIC-Dash), 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 using MIMIC-IV measurements against several ontologies (SNOMED CT, LOINC, etc.). The dashboard is web-based and step-by-step instructions for deployment are provided, simplifying usage for non-technical audiences. The modular code structure enables users to extend upon components, including improving similarity scoring, constructing new plots, or configuring new ontologies.

AnnoDash, an improved clinical terminology annotation tool can facilitate data harmonizing by promoting mapping of clinical data. AnnoDash is freely available at the GitHub repository (https://doi.org/10.5281/zenodo.7534840).

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