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

Antonio Desai, Aurora Zumbo, Mauro Giordano, Pierandrea Morandini, Maria Elena Laino, Elena Azzolini, Andrea Fabbri, Simona Marcheselli, Alice Giotta Lucifero, Sabino Luzzi and Antonio Voza ()
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Antonio Desai: Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy
Aurora Zumbo: Department of Biomedical Sciences, Humanitas University, 20072 Milan, Italy
Mauro Giordano: Department of Advanced Medical and Surgical Sciences, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
Pierandrea Morandini: Artificial Intelligence Center, Humanitas Clinical and Research Center—IRCCS, 20089 Milan, Italy
Maria Elena Laino: Department of Radiology, IRCCS Humanitas Research Hospital, 20089 Milan, Italy
Elena Azzolini: Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy
Andrea Fabbri: Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
Simona Marcheselli: Stroke Unit, IRCCS Humanitas Research Hospital, 20089 Milan, Italy
Alice Giotta Lucifero: Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
Sabino Luzzi: Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
Antonio Voza: Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy

IJERPH, 2022, vol. 19, issue 22, 1-10

Abstract: Background: The possible benefits of using semantic language models in the early diagnosis of major ischemic stroke (MIS) based on artificial intelligence (AI) are still underestimated. The present study strives to assay the feasibility of the word2vec word embedding-based model in decreasing the risk of false negatives during the triage of patients with suspected MIS in the emergency department (ED). Methods: The main ICD-9 codes related to MIS were used for the 7-year retrospective data collection of patients managed at the ED with a suspected diagnosis of stroke. The data underwent “tokenization” and “lemmatization”. The word2vec word-embedding algorithm was used for text data vectorization. Results: Out of 648 MIS, the word2vec algorithm successfully identified 83.9% of them, with an area under the curve of 93.1%. Conclusions: 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.

Keywords: artificial intelligence; emergency department; major ischemic stroke; word2vec (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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