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系统审查机器学习应用于二次预防缺血性中风

Meng Chen¹, Dongbao Qian¹, Yixuan Wang²

¹School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People's Republic of China.

Journal of medical systems

|January 2, 2024

概括

机器学习有助于通过识别个性化治疗和二次预防的风险因素来预测缺血性中风的结果. 然而,许多研究缺乏外部验证和模型解释,阻碍了临床信任和有效性.

科学领域:

神经学神经学
医疗信息学医疗信息学
公共卫生公共卫生

背景情况:

缺血性中风带来预后不佳的高风险,超过90%与可修改因素有关.
机器学习 (ML) 越来越多地用于预测缺血性中风的结果,并为二次预防策略提供信息.

结论:

解决数据限制,验证和模型透明度对于提高基于ML的缺血性中风预后模型的可靠性和临床实用性至关重要.
提高研究可信度将有助于开发和实施有效的二次中风预防措施.

关键词:

缺血性中风是因为缺血性中风.机器学习是机器学习.预测模型的预测模型.风险因素风险因素二次预防二次预防

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Ischemic Stroke l: Introduction

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