ArticleCommunications medicine2026
Stratification of Alzheimer's disease patients using knowledge-guided unsupervised latent factor clustering with electronic health record data.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Representation learning to advance multi-institutional studies with electronic health record data from US and France.Nature communications · 2026Article
- Leveraging electronic health records to examine differential clinical outcomes in people with Alzheimer's disease.Communications medicine · 2026Article
- A Unified Framework for Alzheimer's Disease Knowledge Graphs: Architectures, Principles, and Clinical Translation.Brain sciences · 2025Review
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Authors and funding
9 authors.
Funding
Abstract
backgroundPrognostication for people with Alzheimer's disease (AD) at the point of care could improve clinical management.
methodsIn this retrospective cohort study using the electronic health record (EHR) data from a large healthcare system (2011-2022), we applied an unsupervised latent factor clustering approach guided by knowledge graph embeddings to stratify AD patients into two groups at diagnosis (baseline) using clinical features in the two years preceding diagnosis. We prognosticated the risk of AD-related outcomes (nursing home admission and mortality) for the clusters in survival analyses adjusted for baseline confounders (age, gender, race, ethnicity, healthcare utilization, and comorbidities). To reflect real-world evolution in clinical trajectories, we updated patient stratification for patients remaining at risk one year post-diagnosis and repeated prognostication.
resultsWe stratify 16,411 AD patients into two groups at baseline (41% Group 1, 59% Group 2). Baseline Group 2 has a significantly lower risk of nursing home admission (HR [95% CI] = 0.804 [0.765, 0.844], p < .001) but comparable mortality risk to baseline Group 1 (HR [95% CI] = 1.008 [0.963, 1.056], p = 0.733). We re-stratify the 12,606 patients remaining at risk one year post-diagnosis (46% Group 1, 54% Group 2). Consistent with baseline, the updated Group 2 has a lower risk of nursing home admission (HR [95% CI] = 0.815 [0.766, 0.868], p < .001) but comparable mortality risk (HR [95% CI] = 0.977 [0.922, 1.035], p = .430) to Group 1.
conclusionsPatient stratification enables outcome prognosis for AD patients. While baseline prognostication can guide early treatment and tailored management, dynamic prognostication may inform more timely interventions to improve long-term outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.