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