SAFE-MIL:一种基于风险估计的可统计解释的框架,用于查潜在的向治疗患者
Yanfang Guan1,2,3, Zhengfa Xue1,2, Jiayin Wang1,2
1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Frontiers in genetics
|August 30, 2024
概括
一个新的框架,SAFE-MIL,通过分析患者突变水平,准确评估向治疗中的治疗失败风险. 这种可解释的模型有助于临床决策,并改善了癌症患者的个性化医疗.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 生物统计学 生物统计学
背景情况:
- 针对性疗法为具有特定基因突变的患者提供了显著的好处.
- 突变丰度的患者间变异性导致不同的生存结果,使风险评估复杂化.
- 目前的模型缺乏解释性和合理性,无法预测治疗失败.
研究的目的:
- 开发一个可统计解释的框架来估计向治疗中的治疗失败风险.
- 为应对因突变丰度差异而导致生存益处变化的挑战.
- 在个性化医学中提供一个准确患者分层的工具.
主要方法:
- 开发了SAFE-MIL,这是一个整合多实例学习 (MIL) 与Hosmer-Lemeshow测试的框架.
- 构建了患者有效性标签,并用MIL将患者样本分成小组.
- 设计了一个基于Hosmer-Lemeshow测试的可解释损失函数,用于风险估计.
主要成果:
- SAFE-MIL准确估计药物治疗失败风险,并提供最佳风险分层门.
- 在一项针对457名非小细胞肺癌患者的案例研究中,SAFE-MIL在准确性方面超过了传统的回归方法.
- 该框架有效地捕捉了患者之间的风险变化,提供了统计学解释性.
结论:
- SAFE-MIL提供了一个可解释的计算框架,用于针对性治疗中的风险评估.
- 该模型准确地指导药物使用和患者分层的临床决策.
- SAFE-MIL提高了个性化医学的精度,并且适用于其他患者分层问题.
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