Utilizing machine learning to determine the most appropriate method to predict 90-day home-time after stroke and the contribution of clinically relevant covariates to differences in 90-day home-time among men and women

利用机器学习确定预测中风后 90 天在家时间的最合适方法，以及临床相关协变量对男性和女性 90 天在家时间差异的贡献

• 批准号: 397125
• 负责人: Holodinsky Jessalyn K
• 金额: $ 6.56万
• 依托单位: Institute for Clinical Evaluative Sciences-Toronto
• 依托单位国家: 加拿大
• 项目类别: Fellowship Programs
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-11-01 至 2020-11-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=397125&lang=en
• 关键词: Machine Learning; Patient Outcomes; Sex And Gender; Stroke

Stroke is one of the leading causes of disability and death in Canada. However, stroke affects men and women differently. Women experience greater disability compared to men after stroke. The reasons for these differences are unknown and are difficult to

在加拿大，中风是导致残疾和死亡的主要原因之一。然而，中风对男性和女性的影响不同。与男性相比，女性中风后的残疾程度更高。造成这些差异的原因尚不清楚，也很难