SNOMED CT entity linking challenge.

ABSTRACT:

Objective

This paper presents the results from a competition challenging participants to develop entity linking models using a subset of annotated MIMIC-IV-Note data and the SNOMED CT Terminology.

Materials and methods

As a basis for this work, a large set of 74 808 annotations was curated across 272 discharge notes spanning 6624 unique clinical concepts. Submissions were evaluated using the mean Intersection-over-Union metric, evaluated at the character level with the 3 best performing solutions awarded a cash prize.

Results

The winning solutions employed contrasting approaches: a dictionary-based method, an encoder-based method, and a decoder-based method.

Discussion

Our analysis reveals that concept frequency in training data significantly impacts model perf

SUBMITTER: Davidson R 

PROVIDER: S-EPMC12361850 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature