Alzheimer's disease-oriented biomarker dynamic trajectory prediction method and system
By using a cross-biomarker temporally coupled encoder and a dual-head predictive decoder to process the unequal intervals and missing longitudinal sequences of Alzheimer's disease-related biomarkers, this approach solves the problem in existing technologies that cannot predict future change trajectories and conversion probabilities. It enables explicit modeling of temporal relationships between biomarkers and effective processing of sparse sequences, providing support for optimal timing of clinical intervention.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG GEWUZHIZHI BIOTECHNOLOGY CO LTD
- Filing Date
- 2026-04-20
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies cannot effectively predict the future trajectory and conversion probability of Alzheimer's disease-related biomarkers, especially in asymptomatic or very early-stage individuals. Existing methods lack the ability to process sparse longitudinal sequences and model temporal interactions between biomarkers, and cannot provide clinical support for the optimal timing of intervention.
A cross-marker temporally coupled encoder and a dual-head predictive decoder are constructed. Multi-level dual-axis attention modules are stacked to process longitudinal sequence data of multiple markers with unequal intervals and partial missing data, capture the temporal coupling patterns and hysteresis effects between markers, and output the future change curves and transformation probabilities of each marker.
It enables multi-dimensional prediction of Alzheimer's disease-related biomarkers, provides a two-dimensional assessment of future trends and conversion probabilities, supports clinicians in determining the optimal timing for intervention, and extends the model's predictive robustness in sparse sequence scenarios.
Smart Images
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