AI Article Synopsis
- The study explores the use of AI, specifically semantic language models, to improve the early diagnosis of major ischemic stroke (MIS) in emergency departments.
- Researchers analyzed patient data through text processing techniques and employed the word2vec algorithm to enhance the identification of MIS cases.
- The findings indicate that the algorithm successfully detected 83.9% of MIS cases with a high accuracy, suggesting that NLP models could significantly aid in the triage process and support healthcare professionals.
Article 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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691077 | PMC |
| http://dx.doi.org/10.3390/ijerph192215295 | DOI Listing |
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