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Updated: Sep 15, 2025

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SNOMED CT entity linking challenge.

Rory Davidson1, Will Hardman2, Guy Amit3

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|July 14, 2025
PubMed
Summary
This summary is machine-generated.

Developing clinical entity linking models is challenging for rare concepts. Performance is impacted by concept frequency, entropy, and annotation ambiguity in training data.

Keywords:
SNOMED CTartificial intelligenceentity linkingterminology

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Area of Science:

  • Medical Informatics
  • Natural Language Processing

Background:

  • Clinical entity linking is crucial for organizing electronic health records.
  • Standardizing clinical concepts using terminologies like SNOMED CT is essential for interoperability.

Purpose of the Study:

  • To evaluate different approaches for clinical entity linking using MIMIC-IV-Note data.
  • To identify factors influencing the performance of entity linking models.

Main Methods:

  • A competition was held using a subset of annotated MIMIC-IV-Note data and SNOMED CT.
  • 74,808 annotations across 272 discharge notes were curated.
  • Submissions were evaluated using the mean Intersection-over-Union metric at the character level.

Main Results:

  • Winning solutions utilized diverse methods: dictionary-based, encoder-based, and decoder-based.
  • Concept frequency in training data significantly affected model performance.
  • Rare concepts, high concept entropy, and annotation ambiguity were associated with lower performance.

Conclusions:

  • Future entity linking research should prioritize rare concepts.
  • Developing methods to handle scarce training data and leverage contextual information is recommended.