Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study
By integrating International Classification of Diseases, Ninth Revision codes and LLM-derived embeddings, our analysis delineated 2 distinct ADRD subtypes with sex-specific comorbid and clinical presentations, offering insights for potential precision medicine approaches.
Authors
- Colin Magdamo (ORCID: https://orcid.org/0000-0001-8965-4630)
- Deborah Blacker (ORCID: https://orcid.org/0000-0001-6107-7376)
- Alberto Serrano‐Pozo (ORCID: https://orcid.org/0000-0003-0899-7530)
- Sudeshna Das (ORCID: https://orcid.org/0000-0002-9486-6811)
- Bradley T. Hyman (ORCID: https://orcid.org/0000-0002-7959-9401)
- You Cheng (ORCID: https://orcid.org/0000-0002-3141-0104)
- John R. Dickson (ORCID: https://orcid.org/0000-0003-0135-7928)
- Maximilian West
- Yu Leng (ORCID: https://orcid.org/0000-0002-7039-4127)
- Yingnan He
Institutions
- Massachusetts General Hospital (US)
Publication Details
- Journal
- JMIR Aging
- Published
- 2025-03-31
- DOI
- https://doi.org/10.2196/65178
- Citations
- 5
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 12.40