AD risk score for the early phases of disease based on unsupervised machine learning.

Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
Published Date: Jul 30, 2020

Abstract

INTRODUCTION: Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention.

Authors

  • Zheyu Wang
    Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Zhuojun Tang
    Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Yuxin Zhu
    Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Corinne Pettigrew
    Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Anja Soldan
    Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Alden Gross
    Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, Maryland, USA.
  • Marilyn Albert
    Department of Neurology, Johns Hopkins University, Baltimore, MD, USA.

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