ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
An Interpretable Population Graph Network to Identify Rapid Progression of Alzheimer's Disease Using UK Biobank.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Gut‑bone metabolites: SCFAs, polyamines and microbial metabolomics in osteoporosis risk and therapy (Review).International journal of molecular medicine · 2026Review
- Identifying sex-specific sub-phenotypes of Alzheimer's disease progression using longitudinal electronic health records.EBioMedicine · 2026Article
- An Exploratory Six-Probe Blood RNA Signature for Predicting 12-Month Cognitive Decline Along the Alzheimer's Disease Continuum: An Interpretable Machine Learning Study.Diagnostics (Basel, Switzerland) · 2026Article
- Identifying Sex-Specific Sub-phenotypes of Alzheimer's Disease Progression Using Longitudinal Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- AutoRADP: An Interpretable Deep Learning Framework to Predict Rapid Progression for Alzheimer's Disease and Related Dementias Using Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Functional connectome metrics reveal distinct prognostic subtypes in two Phase 3 gantenerumab trials.Alzheimer's & dementia (New York, N. Y.)Article
Corrections and comments
- Update of
Authors and funding
8 authors.
Funding
Abstract
Alzheimer's disease (AD) manifests with varying progression rates across individuals, necessitating the understanding of their intricate patterns of cognition decline that could contribute to effective strategies for risk monitoring. In this study, we propose an innovative interpretable population graph network framework for identifying rapid progressors of AD by utilizing patient information from electronic health-related records in the UK Biobank. To achieve this, we first created a patient similarity graph, in which each AD patient is represented as a node; and an edge is established by patient clinical characteristics distance. We used graph neural networks (GNNs) to predict rapid progressors of AD and created a GNN Explainer with SHAP analysis for interpretability. The proposed model demonstrates superior predictive performance over the existing benchmark approaches. We also revealed several clinical features significantly associated with the prediction, which can be used to aid in effective interventions for the progression of AD patients.
Indexed as
Identifiers
40417509PMC12099444What OpenQuestion holds
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.