Stratification of Alzheimer’s disease patients using knowledge-guided unsupervised latent factor clustering with electronic health record data

ABSTRACT Prognostication for people with Alzheimer’s disease (AD) at the point of care could improve clinical management. Applying a novel unsupervised latent factor clustering approach guided by knowledge graph embeddings of relevant clinical features from electronic health records, we stratified 16,411 AD patients into two groups at diagnosis and prognosticated their risk of AD-related outcomes ( i.e., nursing home admission, mortality), adjusting for baseline confounders. To reflect real-world evolution in clinical trajectories, we updated patient stratification for 12,606 AD patients remaining at risk 1-year post-diagnosis and repeated prognostication. At both timepoints, one group had a higher nursing home admission risk and exhibited characteristics suggesting greater symptom burden, but the mortality risk remained comparable between groups. This study supports that patient stratification can enable 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.