Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings
arXiv:2606.07798v1 Announce Type: cross Abstract: Alzheimer's disease is a progressive neurodegenerative disorder, and its progression varies substantially across patients. Existing work aims to forecast patients' future cognitive state, with minimal focus on reconstructing the state from past visits. Furthermore, in current research, quantifying predictive uncertainty remains underexplored and relies on costly modalities such as MRI, PET, and CSF, limiting their deployment in resource-limited settings. In this research, our primary objectives are: First, bidirectional prediction of cognitive
The increasing availability of routine clinical data and advancements in AI/ML techniques allow for more sophisticated and accessible disease progression modeling, particularly in underserved regions.
This research addresses a critical gap in Alzheimer's disease management by providing more accessible and less costly methods for predicting and reconstructing disease trajectories, vital for resource-constrained settings.
The ability to utilize standard clinical data for bidirectional prediction of cognitive states in Alzheimer's patients, coupled with uncertainty quantification, expands diagnostic and prognostic capabilities beyond expensive specialized modalities.
- · Patients in resource-constrained settings
- · Healthcare providers
- · AI/ML healthcare developers
- · Public health organizations
- · Manufacturers of expensive diagnostic equipment (MRI, PET, CSF)
Improved early diagnosis and personalized treatment plans for Alzheimer's patients in developing regions become more feasible.
The reduced reliance on costly medical imaging could reallocate healthcare budgets towards other preventative or treatment initiatives.
This model could be widely adapted for other chronic, progressive diseases, catalyzing a paradigm shift in global disease management strategies.
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Read at arXiv cs.LG