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Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becomingabnormal, the pathophysiology of which is very complex and largely unknown.Event-based modeling (EBM) is a data-driven technique to estimate the sequencein which biomarkers for a disease become abnormal based on cross-sectionaldata. It can help in understanding the dynamics of disease progression andfacilitate early diagnosis and prognosis. In this work we propose a noveldiscriminative approach to EBM, which is shown to be more accurate thanexisting state-of-the-art EBM methods. The method first estimates for eachsubject an approximate ordering of events. Subsequently, the central orderingover all subjects is estimated by fitting a generalized Mallows model to theseapproximate subject-specific orderings. We also introduce the concept ofrelative distance between events which helps in creating a disease progressiontimeline. Subsequently, we propose a method to stage subjects by placing themon the estimated disease progression timeline. We evaluated the proposed methodon Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared theresults with existing state-of-the-art EBM methods. We also performed extensiveexperiments on synthetic data simulating the progression of Alzheimer'sdisease. The event orderings obtained on ADNI data seem plausible and are inagreement with the current understanding of progression of AD. The proposedpatient staging algorithm performed consistently better than that ofstate-of-the-art EBM methods. Event orderings obtained in simulationexperiments were more accurate than those of other EBM methods and theestimated disease progression timeline was observed to correlate with thetimeline of actual disease progression. The results of these experiments areencouraging and suggest that discriminative EBM is a promising approach todisease progression modeling.

Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling