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For decades, a variety of predictive approaches have been proposed andevaluated in terms of their prediction capability for Alzheimer's Disease (AD)and its precursor - mild cognitive impairment (MCI). Most of them focused onprediction or identification of statistical differences among differentclinical groups or phases (e.g., longitudinal studies). The continuous natureof AD development and transition states between successive AD related stageshave been overlooked, especially in binary or multi-class classification.Though a few progression models of AD have been studied recently, they weremainly designed to determine and compare the order of specific biomarkers. Howto effectively predict the individual patient's status within a wide spectrumof continuous AD progression has been largely overlooked. In this work, wedeveloped a novel learning-based embedding framework to encode the intrinsicrelations among AD related clinical stages by a set of meaningful embeddingvectors in the latent space (Disease2Vec). We named this process as diseaseembedding. By disease em-bedding, the framework generates a disease embeddingtree (DETree) which effectively represents different clinical stages as a treetrajectory reflecting AD progression and thus can be used to predict clinicalstatus by projecting individuals onto this continuous trajectory. Through thismodel, DETree can not only perform efficient and accurate prediction forpatients at any stages of AD development (across five clinical groups insteadof typical two groups), but also provide richer status information by examiningthe projecting locations within a wide and continuous AD progression process.

Disease2Vec: Representing Alzheimer's Progression via Disease Embedding Tree