Preventing ovarian cancer using machine learning to recommend opportunistic salpingectomy in British Columbia: An exploratory mixed-methods approach

在不列颠哥伦比亚省使用机器学习推荐机会性输卵管切除术来预防卵巢癌：一种探索性的混合方法

• 批准号: 459000
• 负责人: Lukey Alexandra M
• 金额: $ 7.65万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Studentship Programs
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-11-01 至 2024-11-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=459000&lang=en
• 关键词: Cancer Prevention; Machine Learning; Mixed-Methods; Opportunistic Salpingectomy; Ovarian Cancer; Population-Based Data

Of the female reproductive system cancers, ovarian cancer is the deadliest, with a five-year survival rate of less than 50%. Most people with ovarian cancer are diagnosed at a late stage because of vague symptoms and a lack of effective screening practice

在女性生殖系统癌症中，卵巢癌是最致命的，五年存活率不到50%。大多数卵巢癌患者在晚期才被诊断出来，因为症状模糊，缺乏有效的筛查方法。