Disease Normalization with Graph Embeddings
This work addresses the problem of linking disease names to clinical taxonomies for biomedical NLP applications, representing an incremental improvement over existing methods.
The paper tackled disease detection and normalization in biomedical texts by proposing graph embeddings that leverage MeSH's graphical structure and lexical information, resulting in improved disease recognition on the NCBI disease benchmark corpus.
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.