Multilayer Data and Document Stratification for Comorbidity Analysis

Abstract

In this work, we introduce two novel contributions to the study of comorbidity. The first is a new method for finding disease correlations, using a multitude of information sources. In the era of big data, methods such as evidence synthesis enable researchers to exploit many freely available information sources to enrich their analyses. This forms the basis for our method where in lieu of examining one form of evidence, we introduce a novel combination of sources, providing an indirect association between patient genetic data and the scientific literature. Our second contribution is a new method for stratifying the scientific literature when searching for newly discovered disease correlations. Given that the volume of published biomedical literature has increased dramatically, a clinician does not have the ability to read every relevant article. We therefore propose a new way for refining the literature search space to discover recently introduced disease correlations. Results show that our system can produce reasonable hypotheses for disease correlations, and that document stratification is an important aspect to take into account when using scientific literature.