Table 1.
Baseline demographic information of patients who were amyloid-positive at baseline diagnosis.
Fig 1.
Boxplot of the first and second principal components.
Fig 2.
Diagnostic density of VPPCA1 in 448 test set patients.
Fig 3.
a) Relationship between VPPCA1 and age. b) Relationship between VPPCA1 and one-dimensional LDA.
Fig 4.
Complete model performance, feature importance, and task difficulty analysis.
Fig 5.
Performance comparison of different feature configuration models.
Fig 6.
Task-specific performance metrics and feature contribution analysis.
Fig 7.
Feature importance analysis and ablation study results.
Table 2.
Disease diagnosis classification results of test set patients based on decision tree.
Table 3.
Performance comparison of VPPCA and other disease progression models for Alzheimer’s disease diagnostic classification.
Fig 8.
Diagnostic classification performance comparison across different methods.
Fig 9.
VPPCA non-cognitive variable modeling vs. complete variable modeling.
Fig 10.
Distribution of the posterior convergence region of parameters of the vertical hierarchical model.
Fig 11.
Parameter posterior distribution probability diagram of the longitudinal hierarchical model.
Fig 12.
Trajectory diagram of test set patients based on baseline diagnosis classification (follow-up records at least three times).
Table 4.
Diagnostic prediction results of VPPCA and other three methods for different categories.
Fig 13.
Relationship between VPPCA1 and PPCA1.
Fig 14.
Comparison of VPPCA and PPCA uncertainty quantification.