BACKGROUND
Life expectancy is one of the most informative indicators of population health and development. Its stability, which has been observed over time, has made the prediction and forecasting of life expectancy an appealing area of study. However, predicted or estimated values of life expectancy do not tell us about age-specific mortality.

OBJECTIVE
Reliable estimates of age-specific mortality are essential in the study of health inequalities, well-being and to calculate other demographic indicators. This task comes with several difficulties, including a lack of reliable data in many populations. Models that relate levels of life expectancy to a full age-specific mortality profile are therefore important but scarce.

METHODS
We propose a deep neural networks (DNN) model to derive age-specific mortality from observed or predicted life expectancy by leveraging deep-learning algorithms akin to demography’s indirect estimation techniques.

RESULTS
Out-of-sample validation was used to validate the model, and the predictive performance of the DNN model was compared with two state-of-the-art models. The DNN model provides reliable estimates of age-specific mortality for the United States, Italy, Japan, and Russia using data from the Human Mortality Database.



中文翻译：

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利用深度神经网络从出生时的预期寿命估算特定年龄的死亡率（作者：Andrea Nigri、Susanna Levantesi、Josè Manuel Aburto）

背景技术
预期寿命是人口健康和发展的最具信息性的指标之一。随着时间的推移已经观察到它的稳定性，使得预期寿命的预测和预测成为一个有吸引力的研究领域。然而，预期寿命的预测值或估计值并不能告诉我们特定年龄的死亡率。

目标
对特定年龄死亡率的可靠估计对于研究健康不平等、福祉和计算其他人口统计指标至关重要。这项任务有几个困难，包括在许多人群中缺乏可靠的数据。因此，将预期寿命水平与完整的特定年龄死亡率概况联系起来的模型很重要，但很少见。

方法
我们提出了一个深度神经网络 (DNN) 模型，通过利用类似于人口统计学间接估计技术的深度学习算法，从观察或预测的预期寿命中推导出特定年龄的死亡率。

结果
样本外验证用于验证模型，并将 DNN 模型的预测性能与两个最先进的模型进行比较。DNN 模型使用人类死亡率数据库中的数据为美国、意大利、日本和俄罗斯提供了可靠的年龄别死亡率估计值。