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

TL;DR

This paper introduces a deep learning model that estimates biological age by integrating morbidity and mortality data, providing a more accurate aging biomarker than traditional methods based solely on chronological age.

Contribution

It presents a novel deep learning approach that incorporates health, morbidity, and mortality data to better reflect biological aging processes.

Findings

The model outperforms existing methods in discriminating morbidity and mortality.

Biological ages derived from the model show higher correlation with health outcomes.

The approach demonstrates improved accuracy on large population datasets.

Abstract

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.