基于深度学习的芬兰一年死亡率预测是准确但不公平的衰老标记
Andrius Vabalas1, Tuomo Hartonen1, Pekka Vartiainen1,2
1Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
Nature aging
|June 24, 2024
概括
研究人员开发了一个深度学习模型来预测短期死亡风险,作为衰老标志物. 这种新的衰老时钟实现了高准确度,并对各种死亡原因进行了概括.
科学领域:
- 老年学和计算生物学
- 利用先进的机器学习进行衰老研究.
背景情况:
- 短期死亡风险是个体脆弱和衰老的关键指标.
- 现有的衰老时钟主要预测时间年龄或长期死亡率,在短期风险预测中留下了一个差距.
- 老龄化预测模型中的算法公平性在很大程度上仍未得到审查.
研究的目的:
- 开发和评估一个深度学习模型来预测1年死亡风险.
- 评估模型在各种死亡原因和人口群体中的表现.
- 为了调查开发的老化时钟的算法公平性.
主要方法:
- 在芬兰人口 (FinRegistry,n=540万) 的全国纵向数据上训练了一个深度学习模型.
- 该模型包含超过8,000个功能,涵盖多达50年的健康数据.
- 使用曲线下的面积 (AUC) 评估性能,并与基线模型 (年龄和性别) 进行比较.
主要成果:
- 深度学习模型实现了高预测准确度,AUC为0.944,明显超过了基线模型 (AUC=0.897).
- 该模型在50个死亡原因中的45个 (AUC>0.800) 中显示出强烈的概括性,包括COVID-19.
- 根据人口群体的表现不同,年轻的女性表现最好,年长的男性表现最差.
结论:
- 开发的深度学习模型准确预测短期死亡风险,作为潜在的全人口老龄化标志物.
- 算法公平性分析揭示了弱势群体之间的差异,突出了公平公共卫生整合的挑战.
- 该模型预测短期死亡率的能力为评估衰老和脆弱性提供了一种新的方法.
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