PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence.

ABSTRACT: Aging clocks that accurately predict human age based on various biodata types are among the most important recent advances in biogerontology. Since 2016 multiple deep learning solutions have been created to interpret facial photos, omics data, and clinical blood parameters in the context of aging. Some of them have been patented to be used in commercial settings. However, psychological changes occurring throughout the human lifespan have been overlooked in the field of "deep aging clocks". In this paper, we present two deep learning predictors trained on social and behavioral data from Midlife in the United States (MIDUS) study: (a) PsychoAge, which predicts chronological age, and (b) SubjAge, which describes personal aging rate perception. Using 50 distinct features from the MIDUS dataset

SUBMITTER: Zhavoronkov A 

PROVIDER: S-EPMC7762465 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature