# Disease Normalization with Graph Embeddings

arXiv: https://arxiv.org/abs/2010.12925 · HTML: https://mlsanity.com/paper/2010.12925/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

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

- [KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling](https://mlsanity.com/paper/2203.06835/) (2022)
- [GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction](https://mlsanity.com/paper/2112.03002/) (2021)
- [Simple Data Augmentation Techniques for Chinese Disease Normalization](https://mlsanity.com/paper/2306.01931/) (2023)
- [Taxonomy Enrichment with Text and Graph Vector Representations](https://mlsanity.com/paper/2201.08598/) (2022)

## Abstract (arXiv)

The detection and normalization of diseases in biomedical texts are key biomedical natural language processing tasks. Disease names need not only be identified, but also normalized or linked to clinical taxonomies describing diseases such as MeSH. In this paper we describe deep learning methods that tackle both tasks. We train and test our methods on the known NCBI disease benchmark corpus. We propose to represent disease names by leveraging MeSH's graphical structure together with the lexical information available in the taxonomy using graph embeddings. We also show that combining neural named entity recognition models with our graph-based entity linking methods via multitask learning leads to improved disease recognition in the NCBI corpus.