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Mathematical statistical methods for stroke prognosis prediction and their clinical application research
- 1 Faculty of Arts, Mcgill University
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Abstract
Stroke is a serious illness, with a global disability rate of over 50% and a mortality rate of up to 30%, making research on stroke prognosis prediction of significant societal importance. This paper comprehensively analyzes the application of mathematical statistical methods in stroke prognosis prediction, aiming to explore how these methods can enhance the accuracy of prognosis predictions, thereby providing patients with personalized treatment plans and improving their long-term rehabilitation process. Initially, the article introduces the severity of stroke and the importance of prognosis prediction, outlining the diversified development trends in current stroke prognosis prediction research. Subsequently, the article detailedly summarizes 11 statistical methods commonly used in stroke prognosis prediction, dividing these methods into three categories: methods suitable for analysis at the initial stage of treatment, methods suitable for data processing during the mid-study phase, and methods for integrating all data to establish regression models. Through specific case studies, this paper demonstrates the application of these statistical methods in actual research, including the use of descriptive statistics in MRI image analysis, the application of T-tests and ANOVA in comparing different treatment effects, and the importance of regression analysis in establishing prognosis models, including linear regression, logistic regression, and multiple regression analysis when considering multiple independent variables. This research not only provides a precise method for predicting the prognosis of stroke patients but also offers theoretical support for medical teams to formulate personalized treatment plans, enabling researchers to more accurately predict the prognosis of stroke patients, providing more personalized and effective treatment options. This contributes to reducing the risks during the patient’s rehabilitation process and improving the quality of life.
Keywords
Stroke, Prognosis Prediction Techniques, Mathematical Statistics, Regression Analysis
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Cite this article
Li,Z. (2024). Mathematical statistical methods for stroke prognosis prediction and their clinical application research. Theoretical and Natural Science,49,22-29.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Biological Engineering and Medical Science
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