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

TL;DR

This study introduces a novel method combining representation learning and tensor factorization to analyze risk factors and causal relationships between epilepsy subgroups and Alzheimer's disease in a large patient cohort.

Contribution

The paper presents a new approach integrating representation learning with tensor factorization for in-depth risk factor and causal analysis in epilepsy and AD.

Findings

Identified a significant causal link between epilepsy and AD onset (p = 1.92e-51).

Discovered five distinct epilepsy subgroups associated with AD risk.

Demonstrated the effectiveness of the combined method for risk factor analysis.

Abstract

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.