Abstract
ObjectiveTo provide an open-source software package for determining temporal correlations between disease states using longitudinal electronic medical records (EMR).Materials and MethodsWe have developed an R-based package, Disease Correlation Network (DCN), which builds retrospective matched cohorts from longitudinal medical records to assess for significant temporal correlations between diseases using two independent methodologies: Cox proportional hazards regression and random forest survival analysis. This optimizable package has the potential to control for relevant confounding factors such as age, gender, and other demographic and medical characteristics. Output is presented as a DCN which may be analyzed using a JavaScript-based interactive visualization tool for users to explore statistically significant correlations between disease states of interest using graph-theory-based network topology.ResultsWe have applied this package to a longitudinal dataset at Loyola University Chicago Medical Center with 654 084 distinct initial diagnoses of 51 conditions in 175 539 patients. Over 90% of disease correlations identified are supported by literature review. DCN is available for download at https://github.com/qunfengdong/DCN.ConclusionsDCN allows screening of EMR data to identify potential relationships between chronic disease states. This data may then be used to formulate novel research hypotheses for further characterization of these relationships.
Highlights
Many temporal correlations between specific disease states are well recognized
The major components of this software package are described : (i) extracting retrospective matched cohorts from electronic medical records (EMR), (ii) performing Cox Proportional Hazard (Cox-PH) regression, (iii) performing Random Forest (RF) survival analysis, and (iv) exploring the correlations between diseases of interest based on statistical significance and network topology using a customized interactive visualization tool
We have applied our method to a longitudinal de-identified EMR dataset at Loyola University Chicago Medical Center with 654 084 distinct initial disease encounters relating to 51 conditions in 175 539 patients
Summary
Many temporal correlations between specific disease states are well recognized. For example, hypertension may increase the risk of heartVC The Author(s) 2019. Many temporal correlations between specific disease states are well recognized. Hypertension may increase the risk of heart. JAMIA Open, 2019, Vol 2, No 3 disease.[1]. Such temporal correlations have been identified through epidemiological studies focusing on specific pairs of diseases. With the availability of large-scale EMRs, there is unprecedented opportunity to uncover temporal disease correlations which have not previously been recogniz
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