Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning
arXiv:2606.24604v1 Announce Type: new Abstract: Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is. Most deep learning approaches reduce this problem to single-step classification, treating cognitively normal, mild cognitive impairment, and dementia as flat categories while providing limited insight into how uncertainty accumulates across future visits. We propose a probabilistic framework that combines ordinal diagnosis predictio
Advances in deep learning and probabilistic modeling are enabling more sophisticated predictions of disease progression, moving beyond single-step classifications.
Improved predictive models for Alzheimer's progression, especially with uncertainty quantification, can transform early intervention strategies, drug development, and patient care planning.
This approach shifts from 'most likely diagnosis' to 'how a patient may evolve over time' with reliability, fundamentally changing the utility of diagnostic forecasting in chronic neurodegenerative diseases.
- · Neurology researchers
- · Pharmaceutical companies (AD focus)
- · Healthcare AI developers
- · Patients and caregivers
- · Traditional diagnostic methods
- · Clinical trial designs without probabilistic foresight
- · Insurers without dynamic risk models
More accurate and reliable forecasting of Alzheimer's progression will enable earlier and more tailored therapeutic interventions.
The ability to quantify forecast uncertainty could lead to personalized medicine pathways and adaptive clinical trial designs for neurodegenerative diseases.
This could accelerate the development of effective Alzheimer's treatments by improving patient stratification and outcome prediction, ultimately raising global health spending efficiency.
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Read at arXiv cs.AI