适应性转移学习用于时间对事件建模,在疾病风险评估中的应用
medRxiv : the preprint server for health sciences
|June 4, 2025
概括
本研究介绍了CoxTL,这是一种用于在小数据集中进行时间到事件预测的新转移学习方法. 通过利用来自其他群体的数据,CoxTL提高了准确性,超过了预测末期病风险的现有模型.
科学领域:
- 生物统计学 生物统计学
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 在小样本环境中建模时间到事件结果存在重大挑战.
- 现有的方法在数据异质性和源数据集和目标数据集之间潜在的转移方面存在困难.
研究的目的:
- 提出CoxTL,一种新的转移学习方法,用于在小样本环境中进行时间到事件分析.
- 通过计算共变量和概念转移来提高预测准确度.
主要方法:
- 考克斯TL基于考克斯的比例危险模型,包括密度比和重要性加权.
- 它解决了多层次的数据异质性,包括共变量和系数转移.
- 该方法对潜在的模型错误规范具有稳定性.
主要成果:
- 在模拟中,CoxTL显示出更高的预测准确度,特别是多层异质性.
- 在预测西班牙裔人群末期病 (ESRD) 风险时,CoxTL与仅针对目标的模型相比,C指数提高了6.76%.
- 在C指数中,CoxTL的表现高达17.94%,超过了最先进的转移学习方法.
结论:
- 考克斯TL有效地利用源数据来改善小目标群体的时间到事件预测.
- 该方法对数据异质性的稳定性使其适合于现实世界的应用,而传统的考克斯模型却失败了.
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