转换死亡率预测:一个基于变压器的死亡率预测模型.
Jordan Weiss1,2, Alaleh Azhir1,3, Nilam Ram4,5
1Stanford Center on Longevity, Stanford University, Stanford, CA, USA.
概括
一个新的变压器模型通过分析健康数据的长期依赖性,显著改善了死亡率预测. 与传统方法相比,这种先进的方法在预测死亡风险方面有两倍的改进.
科学领域:
- 社会科学和生物科学 社会科学和生物科学
- 人口健康研究研究.
- 老年学是一门学科.
背景情况:
- 死亡率预测在社会科学和生物科学中至关重要.
- 传统模型通常分析单个风险因素和死亡率之间的线性关联.
- 变压器模型通过捕捉多个变量之间的长期依赖来提供一种新的方法.
研究的目的:
- 介绍一种基于变压器的模型来预测死亡率.
- 将模型应用于来自健康和退休研究 (HRS) 的数据.
- 评估模型的性能与传统和机器学习基准.
主要方法:
- 分析了38193名50岁以上的美国成年人从HRS (自1992年以来的纵向,两年一次的调查) 的数据.
- 利用来自国家死亡指数和死后访谈的相关死亡率数据.
- 在29年的时间里,使用变压器架构对126个财务,身体和心理健康风险因素的变化进行了建模.
主要成果:
- 在平均9年的随访期间,死亡人数为17,448人.
- 变压器模型始终优于传统和机器学习方法.
- 在下一波死亡率预测的平均精度得分 (APS) 中实现了两倍的改善.
结论:
- 基于变压器的模型,如BEHRT,显著提高死亡率预测.
- 这些模型为传统方法提供了优越的替代方案.
- 强调变压器神经网络在人口健康研究中的潜力.
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