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Existing studies consider Alzheimer's disease (AD) a comorbidity of epilepsy,but also recognize epilepsy to occur more frequently in patients with AD thanthose without. The goal of this paper is to understand the relationship betweenepilepsy and AD by studying causal relations among subgroups of epilepsypatients. We develop an approach combining representation learning with tensorfactorization to provide an in-depth analysis of the risk factors amongepilepsy patients for AD. An epilepsy-AD cohort of ~600,000 patients wereextracted from Cerner Health Facts data (50M patients). Our experimentalresults not only suggested a causal relationship between epilepsy and lateronset of AD ( p = 1.92e-51), but also identified five epilepsy subgroups withdistinct phenotypic patterns leading to AD. While such findings arepreliminary, the proposed method combining representation learning with tensorfactorization seems to be an effective approach for risk factor analysis.

Combining Representation Learning with Tensor Factorization for Risk Factor Analysis - an application to Epilepsy and Alzheimer's disease