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

Research Instruments, Questionnaires, and Tools (576) Artificial Intelligence (1371) Telehealth and Telemonitoring (1671) Machine Learning (1465) Wearable Devices and Sensors (888) Stroke (163)

Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20641, first published .
Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study

Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study

Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study

Authors of this article:

Eunjeong Park1 Author Orcid Image ;   Kijeong Lee2 Author Orcid Image ;   Taehwa Han3 Author Orcid Image ;   Hyo Suk Nam4 Author Orcid Image
Article Authors Cited by (27) Tweetations (4) Metrics

Journals

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  9. Heo J, Yoo J, Lee H, Lee I, Kim J, Park E, Kim Y, Nam H. Prediction of Hidden Coronary Artery Disease Using Machine Learning in Patients With Acute Ischemic Stroke. Neurology 2022;99(1) View
  10. Site A, Nurmi J, Lohan E. Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis. IEEE Access 2021;9:112221 View
  11. Ruksakulpiwat S, Thongking W, Zhou W, Benjasirisan C, Phianhasin L, Schiltz N, Brahmbhatt S. Machine learning-based patient classification system for adults with stroke: A systematic review. Chronic Illness 2023;19(1):26 View
  12. Liu C, Huang J, Kong W, Chen L, Song J, Yang J, Li F, Zi W. Development and validation of machine learning-based model for mortality prediction in patients with acute basilar artery occlusion receiving endovascular treatment: multicentric cohort analysis. Journal of NeuroInterventional Surgery 2024;16(1):53 View
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  16. Su D, Hu Z, Wu J, Shang P, Luo Z. Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition. Frontiers in Neurorobotics 2023;17 View
  17. Li Q, Chi L, Zhao W, Wu L, Jiao C, Zheng X, Zhang K, Li X. Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis. Frontiers in Neurology 2023;14 View
  18. Razfar N, Kashef R, Mohammadi F. Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets. Sensors 2023;23(12):5513 View
  19. Gu Z, He X, Yu P, Jia W, Yang X, Peng G, Hu P, Chen S, Chen H, Lin Y. Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model. Artificial Intelligence in Medicine 2024;150:102822 View
  20. Sengupta N, Rao A, Yan B, Palaniswami M. A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation. IEEE Access 2024;12:36026 View
  21. Li D, Li W, Zhao Y, Liu X. The Analysis of Deep Learning Recurrent Neural Network in English Grading Under the Internet of Things. IEEE Access 2024;12:44640 View
  22. Zhi S, Hu X, Ding Y, Chen H, Li X, Tao Y, Li W. An exploration on the machine-learning-based stroke prediction model. Frontiers in Neurology 2024;15 View
  23. Yu B, Kaku A, Liu K, Parnandi A, Fokas E, Venkatesan A, Pandit N, Ranganath R, Schambra H, Fernandez-Granda C. Quantifying impairment and disease severity using AI models trained on healthy subjects. npj Digital Medicine 2024;7(1) View
  24. Restrepo-Parra E, Ariza-Colpas P, Torres-Bonilla L, Piñeres-Melo M, Urina-Triana M, Butt-Aziz S. Home Monitoring Tools to Support Tracking Patients with Cardio–Cerebrovascular Diseases: Scientometric Review. IoT 2024;5(3):524 View