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

Geroinformatics and Electronic Clinical Information/Decision Making in Geriatrics (29) Clinical Informatics (1160) Dementia and Cognitive Decline (586) Electronic Health Records (1181) Generative Language Models Including ChatGPT (492) Foundation Models and Their Applications in AI (24) Ontologies, Classifications, and Coding (270) Diagnostic Tools in Mental Health (279)

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 Tweetations Metrics

Matthew West   1 * , MPhys, SM ;   You Cheng   1 * , PhD ;   Yingnan He   1 * , MS ;   Yu Leng   1 , SM ;   Colin Magdamo   1 , BS ;   Bradley T Hyman   1 , MD, PhD ;   John R Dickson   1 , MD, PhD ;   Alberto Serrano-Pozo   1 , MD, PhD ;   Deborah Blacker   1 , MD, PhD ;   Sudeshna Das   1 , PhD

1 Massachusetts General Hospital, Cambridge, MA, United States

*these authors contributed equally

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