  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.
