A Neural Network (NN) approach for the modelling of mortality rates in a multi-population framework is compared to three classical mortality models. The NN setup contains two instances of Recurrent NNs, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) networks. The stochastic approaches comprise the Li and Lee model, the Common Age Effect model of Kleinow, and the model of Plat. All models are applied and compared in a large case study on decades of data of the Italian population as divided in counties. In this case study, a new index of multiple deprivation is introduced and used to classify all Italian counties based on socio-economic indicators, sourced from the local office of national statistics (ISTAT). The aforementioned models are then used to model and predict mortality rates of groups of different socio-economic characteristics, sex, and age.

将多人群框架中死亡率建模的神经网络 (NN) 方法与三种经典死亡率模型进行了比较。神经网络设置包含两个循环神经网络实例，包括长短期记忆 (LSTM) 和门控循环单元 (GRU) 网络。随机方法包括 Li 和 Lee 模型、Kleinow 的共同年龄效应模型和 Plat 模型。所有模型均在针对意大利各县人口数十年数据的大型案例研究中进行应用和比较。在本案例研究中，引入了一种新的多重剥夺指数，并用于根据来自当地国家统计局 (ISTAT) 的社会经济指标对所有意大利县进行分类。然后使用上述模型来建模和预测不同社会经济特征、性别和年龄群体的死亡率。