Identification of preclinical dementia according to ATN classification for stratified trial recruitment: A machine learning approach

There is a strong case for de‐risking neurodegenerative agent development through highly informative experimental medicine studies early in the disease process. These types of studies are dependent on a research infrastructure that includes volunteer registries holding highly granular phenotypic and genotypic data to allow stratified study selection. Examples of such registries include the Brain Health Registry, Great Minds and PROTECT cohorts which rely on remote cognitive, self‐reported medical history and genetic data. This requires the development of effective algorithms to predict the presence of preclinical dementia pathology. In this study we sought to address this need by building a machine learning (ML) ATN risk prediction algorithm which incorporates data typically collected in such registries.