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

arXiv: https://arxiv.org/abs/2405.16115 · HTML: https://mlsanity.com/paper/2405.16115/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 25 May 2024.

## Builds on (most cited first, 8 of 8)

- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval](https://mlsanity.com/paper/2307.00589/) (2023)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/2112.07869/) (2021)
- [Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes](https://mlsanity.com/paper/2309.00237/) (2023)
- [COMETA: A Corpus for Medical Entity Linking in the Social Media](https://mlsanity.com/paper/2010.03295/) (2020)

## Built on by (most cited first, 4 of 4)

- [Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs](https://mlsanity.com/paper/2508.14817/) (2025)
- [Automated SNOMED CT Concept Annotation in Clinical Text Using Bi-GRU Neural Networks](https://mlsanity.com/paper/2508.02556/) (2025)
- [CDE-Mapper: Using Retrieval-Augmented Language Models for Linking Clinical Data Elements to Controlled Vocabularies](https://mlsanity.com/paper/2505.04365/) (2025)
- [Hierarchical Retrieval with Out-Of-Vocabulary Queries: A Case Study on SNOMED CT](https://mlsanity.com/paper/2511.16698/) (2025)

## Abstract (arXiv)

The extraction and analysis of insights from medical data, primarily stored in free-text formats by healthcare workers, presents significant challenges due to its unstructured nature. Medical coding, a crucial process in healthcare, remains minimally automated due to the complexity of medical ontologies and restricted access to medical texts for training Natural Language Processing models. In this paper, we proposed a method, "SNOBERT," of linking text spans in clinical notes to specific concepts in the SNOMED CT using BERT-based models. The method consists of two stages: candidate selection and candidate matching. The models were trained on one of the largest publicly available dataset of labeled clinical notes. SNOBERT outperforms other classical methods based on deep learning, as confirmed by the results of a challenge in which it was applied.