forgot password?
register here
Research articles
  search articles
  reviews guidelines
  articles index
My Pages
my alerts
  my messages
  my reviews
  my favorites
Members: 3652
Articles: 2'545'386
Articles rated: 2609

24 June 2024
  » arxiv » 2302.00319

 Article overview

Development of deep biological ages aware of morbidity and mortality based on unsupervised and semi-supervised deep learning approaches
Seong-Eun Moon ; Ji Won Yoon ; Shinyoung Joo ; Yoohyung Kim ; Jae Hyun Bae ; Seokho Yoon ; Haanju Yoo ; Young Min Cho ;
Date 1 Feb 2023
AbstractBackground: 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.
Source arXiv, 2302.00319
Services Forum | Review | PDF | Favorites   
Visitor rating: did you like this article? no 1   2   3   4   5   yes

No review found.
 Did you like this article?

This article or document is ...
of broad interest:
Global appreciation:

  Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
» my Online CV
» Free

home  |  contact  |  terms of use  |  sitemap
Copyright © 2005-2024 - Scimetrica