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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

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;48(2):3007 View
  3. Breithaupt A, Tang A, Paolillo E, Bibars M, Johnson E, Saloner R, Possin K, Windon C, Hill-Jarrett T, Giorgio J, Rauschecker A, Kwon H, Vonk J, Pinheiro-Chagas P. Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias. Seminars in Neurology 2026;46(01):105 View
  4. Zhu M, Liu Y, Luo Z, Zhu T. Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction. npj Digital Medicine 2026;9(1) View