在时间依赖的生存模型中改进预测性能的统计学习方法
Hyungwoo Seo1, Wonil Chung2,3
1Department of Statistics and Actuarial Science, Soongsil University, Seoul, 06978, South Korea.
Genomics & informatics
|September 2, 2025
概括
改进生存模型中的时间间隔可以改善COVID-19风险评估. 先进的模型和分层间隔提高了传染病发展的预测准确性,当假设得到满足时,其性能优于标准方法.
科学领域:
- 流行病学
- 生物统计学
- 计算生物学
背景情况:
- COVID-19 疫情需要强大的传染病生存模式.
- 标准的考克斯比例危险 (PH) 模型由于不断的共变假设而存在时间依赖的效应.
- 需要先进的模型来准确地捕捉疾病动态和时间变化的风险.
研究的目的:
- 评估和改进生存模型,以评估感染性疾病的时间依赖性影响.
- 为了比较Cox PH,机器学习和深度学习的生存模型的性能.
- 通过改进的建模技术,改进COVID-19变种的风险估计.
主要方法:
- 应用了多层次的Cox PH模型,以满足PH假设.
- 通过模拟评估机器学习 (随机生存森林) 和深度学习 (DeepSurv,DeepHit) 模型.
- 介绍了COVID-19变种综合危险比率的精细时间间隔划分和加权总和方法.
主要成果:
- 增加时间间隔显著提高了预测准确性.
- 当符合PH假设时,Cox PH模型的表现优于ML/DL模型.
- 针对COVID-19变种的精细危险比率显示风险下降:早期 (29.359),欧盟1 (20.734),阿尔法 (4.079).
结论:
- 精确时间间隔可以更好地理解感染性疾病生存分析中的时间依赖性影响.
- 分层间隔和先进模型改善了COVID-19和其他不断发展的疾病的风险评估和预测准确性.
- 这种方法提供了更细致的疾病进展和风险因素随着时间的推移.
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