Modeling Multi-View Dependence in Bayesian Networks for Alzheimer's Disease Detection

Area of Science:

• Neuroscience
• Biomedical Informatics
• Machine Learning

Background:

• Early detection of Alzheimer's disease (AD) is crucial for timely interventions.
• Current diagnostic methods may not fully integrate diverse patient data.
• Identifying distinct disease stages aids in personalized treatment strategies.

Purpose of the Study:

• To develop a multi-view dependence modeling framework for early Alzheimer's disease detection.
• To integrate heterogeneous data types for improved disease staging.
• To create interpretable models for understanding disease progression.

Main Methods:

• Utilized a multi-view dependence modeling framework.
• Integrated neuro-images, biological markers, clinical data, and genotypical information.
• Employed Bayesian Networks for learning data dependence structures and ensuring interpretability.

Main Results:

• The framework successfully distinguished patients across different Alzheimer's disease stages.
• Interpretable models quantified probabilistic dependencies among variables.
• Hybrid dependence models demonstrated enhanced prediction performance.

Conclusions:

• The proposed framework offers a robust approach for early Alzheimer's detection and staging.
• Integrating diverse data sources with interpretable models improves diagnostic capabilities.
• This method holds potential for advancing Alzheimer's disease research and clinical practice.