ArticleInternational journal of environmental research and public health2022
Word2vec Word Embedding-Based Artificial Intelligence Model in the Triage of Patients with Suspected Diagnosis of Major Ischemic Stroke: A Feasibility Study.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Systematic Review of Large Language Models and Natural Language Processing in Stroke Care: Applications, Challenges, and Future Directions.Stroke (Hoboken, N.J.) · 2026Review
- Analysis of the minutes of rondas campesinas using text mining techniques.Frontiers in sociology · 2026Article
- Loc4Lnc: Accurate prediction of long noncoding RNA subcellular localization via enhanced RNA sequence representation.Quantitative biology (Beijing, China) · 2025Article
- Deep Learning Based Models for CRISPR/Cas Off-Target Prediction.Small methods · 2025Review
- Pediatric head trauma algorithm for head CT decision-making in the emergency department.Journal of anesthesia, analgesia and critical care · 2025Article
- Comparative Analysis of Antithrombotic Therapy Outcomes in Mild Traumatic Brain-Injury Patients: A Focus on Bleeding Risk and Hospital-Stay Duration.Life (Basel, Switzerland) · 2024Article
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- MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features.RNA biology · 2024Article
- Natural Language Processing to Extract Meaningful Information from a Corpus of Written Knowledge in Breast Cancer: Transforming Books into Data.Breast care (Basel, Switzerland) · 2023Article
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11 authors at 5 institutions in 1 country.
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Abstract
backgroundThe 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).
methodsThe 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.
resultsOut of 648 MIS, the word2vec algorithm successfully identified 83.9% of them, with an area under the curve of 93.1%.
conclusionsNatural 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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