Background: While deep learning technology, which has the capability of obtaining latent representations based on large-scale data, can be a potential solution for the discovery of a novel aging biomarker, existing deep learning methods for biological age estimation usually depend on chronological ages and lack of consideration of mortality and morbidity that are the most significant outcomes of aging. Methods: This paper proposes a novel deep learning model to learn latent representations of biological aging in regard to subjects' morbidity and mortality. The model utilizes health check-up data in addition to morbidity and mortality information to learn the complex relationships between aging and measured clinical attributes. Findings: The proposed model is evaluated on a large dataset of general populations compared with KDM and other learning-based models. Results demonstrate that biological ages obtained by the proposed model have superior discriminability of subjects' morbidity and mortality.

翻译：背景：深度学习技术有能力基于大规模数据获取潜在表征，这可能是发现新型衰老生物标志物的潜在解决方案。然而，现有的生物学年龄估计深度学习方法通常依赖于实际年龄，且缺乏对衰老最重要结果——发病率和死亡率的考虑。方法：本文提出了一种新颖的深度学习模型，用于学习与受试者发病率和死亡率相关的生物学衰老潜在表征。该模型利用健康检查数据以及发病率和死亡率信息，以学习衰老与测量的临床属性之间的复杂关系。发现：该模型在大型普通人群数据集上进行了评估，并与KDM及其他基于学习的模型进行比较。结果表明，该模型获得的生物学年龄在区分受试者的发病率和死亡率方面具有优越的判别能力。