| Autor: |
Rui Wen, Miaoran Wang, Wei Bian, Haoyue Zhu, Ying Xiao, Jing Zeng, Qian He, Yu Wang, Xiaoqing Liu, Yangdi Shi, Linzhi Zhang, Zhe Hong, Bing Xu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Frontiers in Neurology, Vol 15 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1664-2295 |
| DOI: |
10.3389/fneur.2024.1408457 |
| Popis: |
BackgroundThis investigation seeks to ascertain the efficacy of various machine learning models in forecasting early neurological deterioration (END) following thrombolysis in patients with acute ischemic stroke (AIS).MethodsEmploying data from the Shenyang Stroke Emergency Map database, this multicenter study compiled information on 7,570 AIS patients from 29 comprehensive hospitals who received thrombolytic therapy between January 2019 and December 2021. An independent testing cohort was constituted from 2,046 patients at the First People’s Hospital of Shenyang. The dataset incorporated 15 pertinent clinical and therapeutic variables. The principal outcome assessed was the occurrence of END post-thrombolysis. Model development was executed using an 80/20 split for training and internal validation, employing classifiers like logistic regression with lasso regularization (lasso regression), support vector machine (SVM), random forest (RF), gradient-boosted decision tree (GBDT), and multi-layer perceptron (MLP). The model with the highest area under the curve (AUC) was utilized to delineate feature significance.ResultsBaseline characteristics showed variability in END incidence between the training (n = 7,570; END incidence 22%) and external validation cohorts (n = 2,046; END incidence 10%; p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
