Abstract:
The race to halt Alzheimer's disease (AD) in its tracks demands an early warning system. By predicting which mild cognitive impairment (MCI) patients are likely to decline into AD, clinicians can intervene while the window of opportunity remains open. But how to separate the MCI patients bound for AD from those with more benign forms of impairment? The key lies in examining the factors that influence disease progression. While prior studies have scratched the surface, a comprehensive analysis has proven elusive. Enter the Alzheimer's Disease Neuroimaging Initiative database, which tracks AD progression through a wealth of patient characteristics. Leveraging these rich data, our hybrid approach combines survival analysis with machine learning to generate dynamic predictions of time to AD onset. Rather than merely detecting AD early or diagnosing its current state, our model gazes into the future, forecasting progression from MCI to AD before the disease fully erupts. Among similar efforts, the proposed approach stands apart in scale and accuracy, validated on more patients and with higher predictive power than earlier attempts. Even cognitive tests or brain scans alone can foretell decline, with the proposed work achieving a remarkable C-index of 0.85 when evaluated using the whole ADNI dataset not only a sample from it. By revealing who is likely to convert to AD and when, this work enables clinicians to intervene at the critical junction where MCI transitions to inevitable decline. The future of AD treatment may hinge on such early warnings.
