BioinformaticsbioRxivPreprint
Validating the representation of distance between infarctdiseases using Word2Vec word embedding
Daiki Yokokawa, Kazutaka Noda, Yasutaka Yanagita…·Masatomi Ikusaka
The paper evaluates Word2Vec embeddings for infarction-related diseases, finding that cosine distance best aligns with ICD-10 codes while Euclidean distance reflects word frequency.
DOI 10.1101/2022.06.21.496958