Premium
IC‐P‐144: PRINCIPAL AXES OF PHENOTYPIC VARIABILITY IN ALZHEIMER'S DISEASE DERIVED FROM AN FDG‐PET BASED, UNSUPERVISED MACHINE LEARNING ALGORITHM
Author(s) -
Jones David T.,
Lowe Val J.,
Graff-Radford Jonathan,
Botha Hugo,
Murray Melissa E.,
Parisi Joseph E.,
Josephs Keith A.,
Machulda Mary M.,
Therneau Terry M.,
Przybelski Scott A.,
Senjem Matthew L.,
Kantarci Kejal,
Boeve Bradley F.,
Knopman David S.,
Petersen Ronald C.,
Jack Clifford R.
Publication year - 2018
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2018.06.2210
Subject(s) - artificial intelligence , dimensionality reduction , principal component analysis , neurodegeneration , phenotype , pattern recognition (psychology) , multivariate statistics , unsupervised learning , multivariate analysis , machine learning , disease , medicine , computer science , pathology , biology , genetics , gene
IC-P-144 PRINCIPAL AXES OF PHENOTYPIC VARIABILITY IN ALZHEIMER’S DISEASE DERIVED FROM AN FDG-PET BASED, UNSUPERVISED MACHINE LEARNING ALGORITHM David T. Jones, Val J. Lowe, Jonathan Graff-Radford, Hugo Botha, Melissa E. Murray, Joseph E. Parisi, Keith A. Josephs, Mary M. Machulda, Terry M. Therneau, Scott A. Przybelski, Matthew L. Senjem, Kejal Kantarci, Bradley F. Boeve, David S. Knopman, Ronald C. Petersen, Clifford R. Jack, Jr., Mayo Clinic, Rochester, MN, USA; Mayo Clinic Florida, Jacksonville, FL, USA; Department of Neurology,Mayo Clinic, Rochester,MN,USA. Contact e-mail: jones.david@mayo.edu