Version 1
: Received: 26 August 2024 / Approved: 27 August 2024 / Online: 28 August 2024 (12:35:10 CEST)
How to cite:
Huang, S.; Diao, S.; Wan, Y. Application of Machine Learning Methods in Predicting Functional Recovery in Ischemic Stroke Patients. Preprints2024, 2024081974. https://doi.org/10.20944/preprints202408.1974.v1
Huang, S.; Diao, S.; Wan, Y. Application of Machine Learning Methods in Predicting Functional Recovery in Ischemic Stroke Patients. Preprints 2024, 2024081974. https://doi.org/10.20944/preprints202408.1974.v1
Huang, S.; Diao, S.; Wan, Y. Application of Machine Learning Methods in Predicting Functional Recovery in Ischemic Stroke Patients. Preprints2024, 2024081974. https://doi.org/10.20944/preprints202408.1974.v1
APA Style
Huang, S., Diao, S., & Wan, Y. (2024). Application of Machine Learning Methods in Predicting Functional Recovery in Ischemic Stroke Patients. Preprints. https://doi.org/10.20944/preprints202408.1974.v1
Chicago/Turabian Style
Huang, S., Su Diao and Yajie Wan. 2024 "Application of Machine Learning Methods in Predicting Functional Recovery in Ischemic Stroke Patients" Preprints. https://doi.org/10.20944/preprints202408.1974.v1
Abstract
This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.
Keywords
Machine Learning; Ischemic Stroke; Functional Recovery Prediction; Big Data Analytics
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
