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

Journal: Alzheimer's research & therapy
PMID: 39736679

Abstract

BACKGROUND: 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, there is a lack of deep learning-based survival analysis models that integrate both genetic and clinical factors to develop and validate individualized dynamic dementia risk prediction models.

Authors

  • Shiqi Yuan
    Department of Neurology, The First Affiliated Hospital of Jinan University, No.613, Huangpu Road West, Guangzhou, Guangdong Province, 510630, China.
  • Qing Liu
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China.
  • Xiaxuan Huang
    Department of Neurology, The First Affiliated Hospital of Jinan University, No.613, Huangpu Road West, Guangzhou, Guangdong Province, 510630, China.
  • Shanyuan Tan
    Department of Neurology, The First Affiliated Hospital of Jinan University, No.613, Huangpu Road West, Guangzhou, Guangdong Province, 510630, China.
  • Zihong Bai
    Department of Neurology, The First Affiliated Hospital of Jinan University, No.613, Huangpu Road West, Guangzhou, Guangdong Province, 510630, China.
  • Juan Yu
    College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046 Xinjiang, China.
  • Fazhen Lei
    Department of Neurology, The Second People's Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University), Guiyang, Guizhou Province, 550000, China.
  • Huan Le
    Department of Neurology, The Second People's Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University), Guiyang, Guizhou Province, 550000, China.
  • Qingqing Ye
    Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China.
  • Xiaoxue Peng
    Department of Neurology, The Second People's Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University), Guiyang, Guizhou Province, 550000, China.
  • Juying Yang
    Department of Neurology, The Second People's Hospital of Guiyang (The Affiliated Jinyang Hospital of Guizhou Medical University), Guiyang, Guizhou Province, 550000, China.
  • Yitong Ling
    Department of Neurology, The First Affiliated Hospital of Jinan University, No.613, Huangpu Road West, Guangzhou, Guangdong Province, 510630, China. lingyitong@stu2022.jnu.edu.cn.
  • Jun Lyu
    Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.

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