@article{oubenali_visualization_2022,
	title = {Visualization of medical concepts represented using word embeddings: a scoping review},
	volume = {22},
	shorttitle = {Visualization of medical concepts represented using word embeddings},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962592/},
	doi = {10.1186/s12911-022-01822-9},
	abstract = {Analyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language processing (NLP), ...},
	language = {en},
	urldate = {2023-05-12},
	journal = {BMC Medical Informatics and Decision Making},
	author = {Oubenali, Naima and Messaoud, Sabrina and Filiot, Alexandre and Lamer, Antoine and Andrey, Paul},
	year = {2022},
	pmid = {35351120},
	note = {Publisher: BioMed Central},
	keywords = {Data mining, Databases, Factual, Deep learning, Electronic Health Records, Humans, Medical, Natural Language Processing, Natural language processing, PubMed, Semantics, Visualization, Word embeddings},
}

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