Stratification of Alzheimer's Disease Patients Using Knowledge-Guided Unsupervised Latent Factor Clustering with Electronic Health Record Data
Wang, L.; Venkatesh, S.; Morris, M.; Li, M.; Srivastava, R.; Visweswaran, S.; Lopez, O.; Xia, Z.; Cai, T.
Show abstract
Prognostication for people with Alzheimers 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.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Machine Learning Prediction of Incidence of Alzheimers Disease Using Large-Scale Administrative Health Data 95%
- Interpretable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons in older adults 94%
- A scoping review of remote and unsupervised digital cognitive assessments in preclinical Alzheimer’s disease 92%
Similar papers in this journal
- Using Machine Learning and Electronic Health Record (EHR) Data for the Early Prediction of Alzheimer’s Disease and Related Dementias 94%
- Real-world datasets for the International Registry for Alzheimer’s Disease and Other Dementias (InRAD) and other registries: an international consensus 91%
- Continuous Associations Between Remote Self-Administered Cognitive Measures and Imaging Biomarkers of Alzheimer’s Disease 90%
Similar papers in this journal
- Deep clinical phenotyping of Alzheimer’s Disease Patients Leveraging Electronic Medical Records Data Identifies Sex-Specific Clinical Associations 97%
- AI-driven fusion of neurological work-up for assessment of biological Alzheimer’s disease 94%
- Individual bioenergetic capacity as a potential source of resilience to Alzheimer’s disease 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.