Using Machine Learning and Oscillometry to Predict Graft Failure After Lung Transplantation

使用机器学习和示波法预测肺移植后的移植失败

• 批准号: 417100
• 负责人: Abelson David C
• 金额: $ 7.65万
• 依托单位: Toronto General Research Institute
• 依托单位国家: 加拿大
• 项目类别: Studentship Programs
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-11-01 至 2022-11-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=417100&lang=en
• 关键词: Chronic Lung Allograft Dysfunction; Lung Transplantation; Machine Learning; Oscillometry; Phenotype; Physiology

For patients suffering end-stage lung disease, lung transplant is the only treatment that can restore function and quality of life. However, long-term survival is poor, with half of all transplanted patients dying within 6 years, despite excellent short-t

对于患有终末期肺病的患者，肺移植是唯一可以恢复功能和生活质量的治疗方法。然而，长期生存率很差，尽管有很好的短期生存率，但一半的移植患者在6年内死亡。