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Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression.

Konrad F WaschkiesJoram SochMargarita DarnaAnni RichterSlawek AltensteinAline BeyleFrederic BrosseronFriederike BuchholzMichaela ButrynLaura DobischMichael EwersKlaus FliessbachTatjana GabelinWenzel GlanzDoreen GoerssDaria GrefDaniel JanowitzIngo KilimannAndrea LohseMatthias H MunkBoris-Stephan RauchmannAyda RostamzadehNina RoyEike Jakob SpruthPeter DechentMichael T HenekaStefan HetzerAlfredo RamirezKlaus SchefflerKatharina BuergerChristoph LaskeMatthias BrendelOliver PetersJosef PrillerAnja SchneiderAnnika SpottkeStefan TeipelEmrah DüzelFrank JessenJens WiltfangBjörn H SchottJasmin M Kizilirmak
Published in: International journal of geriatric psychiatry (2023)
Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages.
Keyphrases
  • machine learning
  • deep learning
  • cognitive decline
  • artificial intelligence
  • sleep quality
  • big data
  • high fat diet
  • genome wide
  • type diabetes
  • gene expression
  • adipose tissue
  • metabolic syndrome
  • dna methylation