TY  - JOUR
AU  - West, Matthew
AU  - Cheng, You
AU  - He, Yingnan
AU  - Leng, Yu
AU  - Magdamo, Colin
AU  - Hyman, Bradley T
AU  - Dickson, John R
AU  - Serrano-Pozo, Alberto
AU  - Blacker, Deborah
AU  - Das, Sudeshna
PY  - 2025
DA  - 2025/3/31
TI  - Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study
JO  - JMIR Aging
SP  - e65178
VL  - 8
KW  - Alzheimer disease and related dementias
KW  - electronic health records
KW  - large language models
KW  - clustering
KW  - unsupervised learning
AB  - Background: Alzheimer disease and related dementias (ADRD) exhibit prominent heterogeneity. Identifying clinically meaningful ADRD subtypes is essential for tailoring treatments to specific patient phenotypes. Objective: We aimed to use unsupervised learning techniques on electronic health records (EHRs) from memory clinic patients to identify ADRD subtypes. Methods: We used pretrained embeddings of non-ADRD diagnosis codes (International Classification of Diseases, Ninth Revision) and large language model (LLM)–derived embeddings of clinical notes from patient EHRs. Hierarchical clustering of these embeddings was used to identify ADRD subtypes. Clusters were characterized regarding their demographic and clinical features. Results: We analyzed a cohort of 3454 patients with ADRD from a memory clinic at Massachusetts General Hospital, each with a specialist diagnosis. Clustering pretrained embeddings of the non-ADRD diagnosis codes in patient EHRs revealed the following 3 patient subtypes: one with skin conditions, another with psychiatric disorders and an earlier age of onset, and a third with diabetes complications. Similarly, using LLM-derived embeddings of clinical notes, we identified 3 subtypes of patients as follows: one with psychiatric manifestations and higher prevalence of female participants (prevalence ratio: 1.59), another with cardiovascular and motor problems and higher prevalence of male participants (prevalence ratio: 1.75), and a third one with geriatric health disorders. Notably, we observed significant overlap between clusters from both data modalities (χ24=89.4; P<.001). Conclusions: 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. 
SN  - 2561-7605
UR  - https://aging.jmir.org/2025/1/e65178
UR  - https://doi.org/10.2196/65178
DO  - 10.2196/65178
ID  - info:doi/10.2196/65178
ER  -