Clinical Abbreviation Disambiguation Using Neural Word Embeddings

By Yonghui Wu, Jun Xu, Yaoyun Zhang, Hua Xu. 2015

101 citations recorded by the source corpus.

This study examined the use of neural word embeddings for clinical abbreviation disambiguation, a special case of word sense disambiguation (WSD). We investigated three different methods for deriving word embeddings from a large unlabeled clinical corpus: one existing method called Surrounding based embedding feature (SBE), and two newly developed methods: Left-Right surrounding based embedding feature (LR_SBE) and MAX surrounding based embedding feature (MAX_SBE). We then added these word embeddings as additional features to a Support Vector Machines (SVM) based WSD system. Evaluation using the clinical abbreviation datasets from both the Vanderbilt University and the University of Minnesota showed that neural word embedding features improved the performance of the SVMbased clinical abbreviation disambiguation system. More specifically, the new MAX_SBE method outperformed the other two methods and achieved the state-of-the-art performance on both clinical abbreviation datasets.

Subfields: Artificial Intelligence, Language and Linguistics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Language and Linguistics

David A. Sontag

In Molecular Biology

Citations in the corpus

Large language models are few-shot clinical information extractors (2022, methodology): “For example, clinical neural word embeddings were trained following word2vec (Mikolov et al., 2013; Wu et al., 2015; Roberts, 2016).”
A Comparison of Word Embeddings for the Biomedical Natural Language Processing (2018, background): “Recently in the biomedical domain word embeddings have been remarkably utilized in applications like biomedical named entity recognition (NER) [14], [15], medical synonym extraction[16], relation extraction (RE) including chemical-disease relation [17], drugdrug interaction [18], [19] and protein-pr”
A clinical text classification paradigm using weak supervision and deep representation (2019, methodology): “Word embeddings have been utilized in various clinical NLP applications, such as clinical abbreviation disambiguation [40], named entity recognition [41], and information retrieval [42].”
Deep learning in clinical natural language processing: a methodical review (2019, background): “The figure also reveals a “long tail” of papers on more variegated tasks, include Information Retrieval, 86,88–90 Natural Language Generation, 31,40,91 Abbreviation Disambiguation, 57,68 Section Boundary Detection, 92,93 Shallow Parsing, 94 Machine Translation, 95 Spelling Correction, 96 and others.”
Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients (2017, background): “recognition [45,46], adverse drug event detection [47,48], ranking biomedical synonyms [49], and disambiguating clinical abbreviations [50,51].”