JMIR Publications

ABSTRACT

Background:

In the past 20 years, various methods have been introduced to construct disease networks. However, established disease networks have not been clinically useful to date because of differences among demographic factors as well as temporal order and intensity among disease-disease associations.

Objective:

This study sought to investigate the overall patterns of the association among diseases; network properties such as clustering, degree, and strength; and the relationship between the structure of disease networks and demographic factors.

Methods:

We used National Health Insurance Service - National Sample Cohort (NHIS-NSC) data from the Republic of Korea, which include time series insurance information of one million out of 50 million Korean (approximately 2%) patients collected between 2002 and 2013. After setting the observation and outcome periods, we selected only 520 common Korean Classification of Disease, sixth revision codes that were the most prevalent diagnoses making up approximately 80% of the cases, for statistical validity. Using these data, we constructed a directional and weighted temporal network that considered both demographic factors and network properties.

Results:

Our disease network contained 294 nodes and 3,085 edges a relative risk value of more than 4 and a false-discovery rate-adjusted P-value of less than .001. Interestingly, our network presented four large clusters. Analysis of the network topology revealed a stronger correlation between in-strength and out-strength than between in-degree and out-degree. Further, the mean age of each disease population was related to the position along the regression line of the out/in-strength plot. Conversely, clustering analysis suggested that our network boasted four large clusters with different sex, age, and disease categories.

Conclusions:

We constructed a directional and weighted disease network visualizing demographic factors. Our proposed disease network model is expected to be a valuable tool for use by early clinical researchers seeking to explore the relationships among diseases in the future.