Word2vec Word Embedding-Based Artificial Intelligence Model in the Triage of Patients with Suspected Diagnosis of Major Ischemic Stroke

A Feasibility Study

Bibliographic Data

ID15470977
AuthorsAntonio Desai (0000-0003-4674-4905, Humanitas University), Aurora Zumbo (Humanitas University), Mauro Giordano (0000-0001-8175-547X, University of Campania "Luigi Vanvitelli"), Pierandrea Morandini (0000-0002-8615-3766, Humanitas University), Maria Elena Laino (0000-0001-7964-8798, IRCCS Humanitas Research Hospital), Elena Azzolini (0000-0003-1257-8929, IRCCS Humanitas Research Hospital), Andrea Fabbri (0000-0002-9837-4638, University of Rome Tor Vergata), Simona Marcheselli (0000-0002-7238-4088, IRCCS Humanitas Research Hospital), Alice Giotta Lucifero (0000-0003-1319-9170, University of Pavia), Sabino Luzzi (0000-0002-1381-8528, University of Pavia), Antonio Voza (0000-0001-7309-6929, Humanitas University, corresponding author)
Year2022
Volume19
Issue22
Pages15295-15295
Publication date2022-11-19
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph192215295
PMID36430014
OpenAlexW4309787636
LanguageEN
Citations received1
References cited32

Natural language processing (NLP)-based models in triage have the potential to improve the early detection of MIS and to actively support the clinical staff

Embedding · Lexical analysis · Machine learning · Medical emergency · Natural language processing · Triage · Vectorization (mathematics · Word (group theory · Word embedding · Word2vec · Acute Ischemic Stroke Management · Artificial Intelligence in Healthcare and Education · Computer Science · Machine Learning in Healthcare · Medicine · Artificial Intelligence

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