Learning the irreversible progression trajectory of Alzheimer's disease
This work addresses the need for trustworthy and interpretable models for Alzheimer's disease progression prediction, which is incremental as it builds on existing classification techniques by adding a monotonicity constraint.
The paper tackled the problem of predicting Alzheimer's disease progression by addressing fluctuating risk scores in existing models, proposing a novel regularization approach that enforces monotonicity to align with the irreversible nature of the disease, resulting in improved capture of disease progressiveness while maintaining prediction accuracy on ADNI data.
Alzheimer's disease (AD) is a progressive and irreversible brain disorder that unfolds over the course of 30 years. Therefore, it is critical to capture the disease progression in an early stage such that intervention can be applied before the onset of symptoms. Machine learning (ML) models have been shown effective in predicting the onset of AD. Yet for subjects with follow-up visits, existing techniques for AD classification only aim for accurate group assignment, where the monotonically increasing risk across follow-up visits is usually ignored. Resulted fluctuating risk scores across visits violate the irreversibility of AD, hampering the trustworthiness of models and also providing little value to understanding the disease progression. To address this issue, we propose a novel regularization approach to predict AD longitudinally. Our technique aims to maintain the expected monotonicity of increasing disease risk during progression while preserving expressiveness. Specifically, we introduce a monotonicity constraint that encourages the model to predict disease risk in a consistent and ordered manner across follow-up visits. We evaluate our method using the longitudinal structural MRI and amyloid-PET imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our model outperforms existing techniques in capturing the progressiveness of disease risk, and at the same time preserves prediction accuracy.