ArticleGeroScience2026
Deep learning predicts onset acceleration of 38 age-associated diseases from blood and body composition biomarkers in the UK Biobank.
Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Role of age in the association of chronic conditions and multimorbidity with incident ADL disability: multicohort analyses of 108,810 adults.GeroScience · 2026Article
- Deep sequence learning with multi-task supervision for scalable population health monitoring.Frontiers in public health · 2026Article
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Authors and funding
6 authors.
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Abstract
A major challenge in multimorbid aging is understanding how diseases co-occur and identifying high-risk groups for accelerated disease development, but to date associations in the relative onset acceleration of disease diagnoses have not been used to characterize disease patterns. This study presents the development and evaluation of a neural network Cox model for predicting onset acceleration risk for age-associated conditions, using demographic, anthropomorphic, imaging, and blood biomarker traits from 60,396 individuals and 218,530 outcome events from the UK Biobank. Risk prediction was evaluated with Harrell's concordance index (C-index). The model performed well on internal (C-index
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