Machine learning-based prediction of early neurological deterioration after intravenous thrombolysis for stroke: insights from a large multicenter study
Our research establishes the MLP and lasso regression as robust tools for predicting early neurological deterioration in acute ischemic stroke patients following thrombolysis. Their superior predictive accuracy, compared to traditional models, highlights the significant potential of machine learning approaches in refining prognosis and enhancing clinical decisions in stroke care management. This advancement paves the way for more tailored therapeutic strategies, ultimately aiming to improve patient outcomes in clinical practice.
Authors
- Wei Bian (ORCID: https://orcid.org/0000-0003-4252-047X)
- Wen Rui (ORCID: https://orcid.org/0000-0002-1616-0469)
- Miaoran Wang (ORCID: https://orcid.org/0000-0002-7561-7086)
- Linzhi Zhang (ORCID: https://orcid.org/0000-0002-3311-3584)
- Ying Xiao (ORCID: https://orcid.org/0000-0001-8558-6394)
- Xiaoqing Liu (ORCID: https://orcid.org/0000-0001-8162-0903)
- Yu Wang (ORCID: https://orcid.org/0009-0007-4877-221X)
- Qian He (ORCID: https://orcid.org/0000-0001-9728-7113)
- Jing Zeng (ORCID: https://orcid.org/0009-0000-4100-5869)
- Bing Xu (ORCID: https://orcid.org/0000-0003-0236-7896)
- Hong Zhe (ORCID: https://orcid.org/0000-0002-5167-4748)
- Yangdi Shi
- Haoyue Zhu
Institutions
- Shenyang Medical College (CN)
- Dalian Medical University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Shenyang First People's Hospital (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2024-09-09
- DOI
- https://doi.org/10.3389/fneur.2024.1408457
- Citations
- 5
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.93