Danish municipalities monitor older persons who are at high risk of declining health and would later need home care services. However, there is no established strategy yet on how to accurately identify those who are at high risk. Therefore, there is great potential to optimise the municipalities’ prevention strategies. Denmark’s comprehensive set of electronic population registers provide longitudinal data that cover individual and household socio-demographics and medical history. Using these data, we developed and applied recurrent neural networks to predict the risk of a need of care services in the future and thus identify individuals who would benefit the most from the municipalities’ prevention strategies. We compared our recurrent neural network model to prediction models based on Cox regression and Fine–Gray regression in terms of calibration and discrimination. Challenges for the prediction modelling were the competing risk of death and the longitudinal information on the registered life course data.

中文翻译：

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老龄化社会的个性化护理需求：基于登记数据的预测工具的制作

丹麦市政当局监测处于健康状况恶化风险中且日后需要家庭护理服务的老年人。但是，目前还没有关于如何准确识别高危人群的既定策略。因此，优化市政当局的预防策略具有很大的潜力。丹麦全面的电子人口登记册提供涵盖个人和家庭社会人口统计学和病史的纵向数据。使用这些数据，我们开发并应用了循环神经网络来预测未来需要护理服务的风险，从而确定最能从市政当局的预防策略中受益的个人。我们在校准和辨别方面将我们的循环神经网络模型与基于 Cox 回归和 Fine-Gray 回归的预测模型进行了比较。预测建模面临的挑战是死亡的竞争风险和注册生命历程数据的纵向信息。