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

Autor:	Desai A; Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy.; Department of Biomedical Sciences, Humanitas University, 20072 Milan, Italy., Zumbo A; Department of Biomedical Sciences, Humanitas University, 20072 Milan, Italy.; Internal Medicine, Humanitas Research Hospital, 20089 Milan, Italy., Giordano M; Department of Advanced Medical and Surgical Sciences, University of Campania 'L. Vanvitelli', 80138 Naples, Italy., Morandini P; Artificial Intelligence Center, Humanitas Clinical and Research Center-IRCCS, 20089 Milan, Italy., Laino ME; Department of Radiology, IRCCS Humanitas Research Hospital, 20089 Milan, Italy., Azzolini E; Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy.; Health Directorate, IRCCS Humanitas Research Hospital, 20089 Milan, Italy., Fabbri A; Department of Systems Medicine, University of Rome 'Tor Vergata', 00133 Rome, Italy., Marcheselli S; Stroke Unit, IRCCS Humanitas Research Hospital, 20089 Milan, Italy., Giotta Lucifero A; Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy., Luzzi S; Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy.; Neurosurgery Unit, Department of Surgical Sciences, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy., Voza A; Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy.; Department of Biomedical Sciences, Humanitas University, 20072 Milan, Italy.
Jazyk:	angličtina
Zdroj:	International journal of environmental research and public health [Int J Environ Res Public Health] 2022 Nov 19; Vol. 19 (22). Date of Electronic Publication: 2022 Nov 19.
DOI:	10.3390/ijerph192215295
Abstrakt:	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.
Databáze:	MEDLINE
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