Machine Learning to Stratify Asymptomatic Alzheimer’s Disease Progression Risk with Cerebrospinal Fluid Biomarkers

Change in protein regulation begin years before Alzhiemer’s Disease (AD) functional symptom onset. The period where changes in proteins is detectable without overt cognitive dysfunction is often referred to as the asymptomatic AD stage. The ability to utilize biomarkers to determine who is most likely to transition to symptomatic AD is critical for potential future intervention development. Machine learning provides a means to stratify the asymptomatic AD population using measured biomarker levels.