Key result
AI triage model identifies ~84% of major ischemic stroke cases.
Observational (n=648)
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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.
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Desai et al. (2022) conducted an observational in Suspected major ischemic stroke (n=648). Word2vec word embedding-based artificial intelligence model was evaluated on Successful identification of major ischemic stroke (AUC 93.1%). A word2vec word embedding-based AI model successfully identified 83.9% of major ischemic stroke cases in triage, achieving an area under the curve of 93.1%.
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