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Summary of Snobert: a Benchmark For Clinical Notes Entity Linking in the Snomed Ct Clinical Terminology, by Mikhail Kulyabin et al.


SNOBERT: A Benchmark for clinical notes entity linking in the SNOMED CT clinical terminology

by Mikhail Kulyabin, Gleb Sokolov, Aleksandr Galaida, Andreas Maier, Tomas Arias-Vergara

First submitted to arxiv on: 25 May 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
A BERT-based model called “SNOBERT” is proposed for linking text spans in medical notes to specific concepts in the SNOMED CT ontology. The method consists of two stages: candidate selection and candidate matching, and is trained on one of the largest publicly available datasets of labeled clinical notes. SNOBERT outperforms other classical methods based on deep learning, as confirmed by the results of a challenge.
Low GrooveSquid.com (original content) Low Difficulty Summary
Medical data stored in free-text formats can be hard to analyze because it’s not structured. Doctors use codes to describe patient information, but this process is mostly done manually. This makes it difficult for computers to understand medical texts and train Natural Language Processing models. To solve this problem, researchers developed a new method called “SNOBERT” that links text in clinical notes to specific medical concepts. The method is trained on a large dataset of labeled notes and does better than other computer learning methods.

Keywords

» Artificial intelligence  » Bert  » Deep learning  » Natural language processing