This paper is in the following e-collection/theme issue:

Geroinformatics and Electronic Clinical Information/Decision Making in Geriatrics (32) Clinical Informatics (1257) Dementia and Cognitive Decline (656) Electronic Health Records (1272) Generative Language Models Including ChatGPT (629) Foundation Models and Their Applications in AI (46) Ontologies, Classifications, and Coding (309) Diagnostic Tools in Mental Health (308)

Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65178, first published .
Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study

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

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

Authors of this article:

Matthew West1 Author Orcid Image ;   You Cheng1 Author Orcid Image ;   Yingnan He1 Author Orcid Image ;   Yu Leng1 Author Orcid Image ;   Colin Magdamo1 Author Orcid Image ;   Bradley T Hyman1 Author Orcid Image ;   John R Dickson1 Author Orcid Image ;   Alberto Serrano-Pozo1 Author Orcid Image ;   Deborah Blacker1 Author Orcid Image ;   Sudeshna Das1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Fang S, Yin Z, Cai Q, Li L, Zheng P, Chen L. Harnessing artificial intelligence for brain disease: advances in diagnosis, drug discovery, and closed-loop therapeutics. Frontiers in Neurology 2025;16 View
  2. Stern A, Linial M. Integrative machine learning approach to risk prediction for dementia and Alzheimer’s disease. GeroScience 2025 View