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

Digital Health Reviews (1477) Clinical Information and Decision Making (1601) Decision Support for Health Professionals (1321) Machine Learning (1683) Artificial Intelligence (1662) Precision Medicine (94)

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 (3) Tweetations (4) 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