Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors

• Published in: Alzheimer's research & therapy Vol. 16; no. 1; pp. 278 - 17
• Main Authors: Yuan, Shiqi, Liu, Qing, Huang, Xiaxuan, Tan, Shanyuan, Bai, Zihong, Yu, Juan, Lei, Fazhen, Le, Huan, Ye, Qingqing, Peng, Xiaoxue, Yang, Juying, Ling, Yitong, Lyu, Jun
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 30.12.2024; BioMed Central; BMC
• Subjects: Aged; Deep Learning; DeepSurv; Dementia; Dementia - epidemiology; Dementia - genetics; Environmental aspects; Female; Genetic aspects; Humans; Machine learning; Male; Middle Aged; Proportional Hazards Models; Prospective Studies; Risk Assessment - methods; Risk Factors; Risk prediction model; Survival Analysis; United Kingdom - epidemiology; United Kingdom; China; Risk prediction model; Survival analysis; DeepSurv; Dementia

Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the most advanced technology for survival analysis to date. However,...