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. 2022 Jan 14:289:5-8.
doi: 10.3233/SHTI210845.

A Comparison of Word Embeddings to Study Complications in Neurosurgery

Gleb Danilov  1 , Konstantin Kotik  1 , Michael Shifrin  1 , Yulia Strunina  1 , Tatyana Pronkina  1 , Tatyana Tsukanova  1 , Timur Ishankulov  1 , Maria Shults  1 , Elizaveta Makashova  1 , Yaroslav Latyshev  1 , Rinat Sufianov  1 , Oleg Sharipov  1 , Anton Nazarenko  1 , Nikolay Konovalov  1 , Alexander Potapov  1
Affiliations

A Comparison of Word Embeddings to Study Complications in Neurosurgery

Gleb Danilov et al. Stud Health Technol Inform. .

Abstract

Our study aimed to compare the capability of different word embeddings to capture the semantic similarity of clinical concepts related to complications in neurosurgery at the level of medical experts. Eighty-four sets of word embeddings (based on Word2vec, GloVe, FastText, PMI, and BERT algorithms) were benchmarked in a clustering task. FastText model showed the best close to the medical expertise capability to group medical terms by their meaning (adjusted Rand index = 0.682). Word embedding models can accurately reflect clinical concepts' semantic and linguistic similarities, promising their robust usage in medical domain-specific NLP tasks.

Keywords: NLP; Neurosurgery; clustering; complications; word embeddings.

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