This paper is in the following e-collection/theme issue:

Digital Health Reviews (1803) Clinical Information and Decision Making (2043) Decision Support for Health Professionals (1617) Machine Learning (2202) Artificial Intelligence (2310) Precision Medicine (183)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44895, first published .
The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis

Authors of this article:

Jing Feng1 Author Orcid Image ;   Qizhi Zhang1 Author Orcid Image ;   Feng Wu2 Author Orcid Image ;   Jinxiang Peng3 Author Orcid Image ;   Ziwei Li4 Author Orcid Image ;   Zhuang Chen5 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

Journals

  1. Xu F, Dai Z, Ye Y, Hu P, Cheng H. Bibliometric and visualized analysis of the application of artificial intelligence in stroke. Frontiers in Neuroscience 2024;18 View
  2. Li Y, Jin N, Zhan Q, Huang Y, Sun A, Yin F, Li Z, Hu J, Liu Z. Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis. Frontiers in Endocrinology 2025;16 View
  3. Lou J, Yu X, Ying J, Song D, Xiong W. Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004–2023). Frontiers in Neurology 2025;16 View
  4. Wang B, Jiang B, Liu D, Zhu R. Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e71654 View
  5. Xu L, Chen Z, Zhu D, Wang Y. The Application Status of Radiomics-Based Machine Learning in Intrahepatic Cholangiocarcinoma: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e69906 View
  6. Zhang J, Zhu H, Wu H, Xie H, Lin D, Zhu L. Radiomics and ischemic stroke research: bibliometric insights and visual trends (2004–2024). Frontiers in Neurology 2025;16 View
  7. Zakariaee S, Kadir D, Molazadeh M, Abdi S. The best diagnostic approach for classifying ischemic stroke onset time: A systematic review and meta-analysis. Neuroradiology 2025;67(9):2267 View
  8. Fan Z, Chen Q, Lu W, Yao Z, Yang S, Zhao H, Cao H. Collaborative artificial intelligence for the diagnosis and management of acute ischemic stroke. Annals of Medicine 2026;58(1) View
  9. Cai J, Zhao L, Wang L, Yang W, Gao L, You C. Construction of a prediction model for large-artery atherosclerosis ischemic stroke based on blood biomarkers and ultrasonic imaging. BMC Neurology 2026;26(1) View
  10. Zhang K, Chen R, Yang J, Yan Y, Liu L, Meng C, Li P, Xing G, Liu X. Machine Learning Models for Mortality Prediction in Intensive Care Unit Patients With Ischemic Stroke Associated With Intracranial Artery Stenosis: Retrospective Cohort Study. JMIR Cardio 2026;10:e82042 View
  11. Zheng X, Zhao M, Yang Z, Kang L, Li R, Gao Y, Zhang G, Lai X. Prediction models for early neurological deterioration in patients with acute ischemic stroke: a systematic review and critical appraisal. Frontiers in Neurology 2026;17 View