Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study.

Journal: JMIR aging
PMID: 40163031

Abstract

BACKGROUND: Alzheimer disease and related dementias (ADRD) exhibit prominent heterogeneity. Identifying clinically meaningful ADRD subtypes is essential for tailoring treatments to specific patient phenotypes.

Authors

  • Matthew West
    Massachusetts General Hospital, Cambridge, MA, United States.
  • You Cheng
    The Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China.
  • Yingnan He
    Massachusetts General Hospital, Cambridge, MA, United States.
  • Yu Leng
    Massachusetts General Hospital, Cambridge, MA, United States.
  • Colin Magdamo
    Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
  • Bradley T Hyman
  • John R Dickson
    Massachusetts General Hospital, Cambridge, MA, United States.
  • Alberto Serrano-Pozo
    Massachusetts General Hospital, Cambridge, MA, United States.
  • Deborah Blacker
    Department of Epidemiology, Harvard T. H. Chan School of Public Health and Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Sudeshna Das
    Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

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