Combining Representation Learning with Tensor Factorization for Risk Factor Analysis - an application to Epilepsy and Alzheimer's disease
This provides insights into risk factors for Alzheimer's disease in epilepsy patients, though findings are preliminary and incremental in method.
The paper tackled understanding the relationship between epilepsy and Alzheimer's disease by analyzing causal relations among epilepsy patient subgroups, finding a significant causal link (p = 1.92e-51) and identifying five distinct phenotypic subgroups leading to AD.
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.