  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.
