Bayesian Networks with Latent Time Embedding for Stage-Aware Causal Modeling of Alzheimer's Disease Progression
arXiv:2606.15784v1 Announce Type: new Abstract: Alzheimer's disease (AD) progression is often described through the amyloid-tau-neurodegeneration, or AT(N), cascade. However, most longitudinal models represent this cascade either as a fixed sequence of biomarkers or as a black-box forecasting task. This makes it difficult to determine when biologically guided biomarker relationships influence future regional pathology. In this study, we introduce Bayesian Networks with Latent Time Embedding (BN-LTE), a Bayesian structural framework for stage-aware modeling of AD progression. BN-LTE estimates d
The continuous development in AI and machine learning techniques, specifically in Bayesian networks and latent variable models, enables more sophisticated approaches to complex biological modeling.
This research provides a more nuanced and potentially predictive model for Alzheimer's disease progression, which could revolutionize diagnostic and therapeutic strategies for a major global health challenge.
The ability to model stage-aware causal relationships in AD progression moves beyond black-box forecasting, offering deeper insights into biomarker interactions and the disease's trajectory.
- · Pharmaceutical companies developing AD therapeutics
- · Healthcare providers for improved diagnostic tools
- · AI/ML researchers in biomedical applications
- · Patients with Alzheimer's disease
- · Traditional, less data-driven diagnostic methods for AD
More accurate predictions of individual patient progression in Alzheimer's disease become possible.
This improved understanding could accelerate the development and targeting of personalized therapeutic interventions for AD.
The success of this model might inspire similar AI-driven approaches for understanding and treating other complex, progressive diseases.
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Read at arXiv cs.LG