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Background: While deep learning technology, which has the capability ofobtaining latent representations based on large-scale data, can be a potentialsolution for the discovery of a novel aging biomarker, existing deep learningmethods for biological age estimation usually depend on chronological ages andlack of consideration of mortality and morbidity that are the most significantoutcomes of aging. Methods: This paper proposes a novel deep learning model tolearn latent representations of biological aging in regard to subjects'morbidity and mortality. The model utilizes health check-up data in addition tomorbidity and mortality information to learn the complex relationships betweenaging and measured clinical attributes. Findings: The proposed model isevaluated on a large dataset of general populations compared with KDM and otherlearning-based models. Results demonstrate that biological ages obtained by theproposed model have superior discriminability of subjects' morbidity andmortality.

Development of deep biological ages aware of morbidity and mortality based on unsupervised and semi-supervised deep learning approaches