The Center for Health Systems Innovation at Oklahoma State University has been given a massive data warehouse by Cerner Corporation, a major electronic medical records (EMRs) provider, to help develop analytic applications. The data warehouse contains EMRs on the visits of more than 50 million unique patients across U.S. hospitals (2000-2015). It is the largest and the industry's only relational database that includes comprehensive records with pharmacy, laboratory, clinical events, admissions, and billing data. The database also includes more than 2.4 billion laboratory results and more than 295 million orders for nearly 4,500 drugs by name and brand. It is one of the largest compilations of de-identified, real-world, HIPAA-compliant data of its type.
The EMRs can be used to develop multiple analytics applications. One application is to understand the relationships between diseases based on the information about the simultaneous diseases developed in the patients. When multiple diseases are present in a patient, the condition is called comorbidity.
The comorbidities can be different across population groups. In an application (Kalgotra, Sharda, & Croff, 2017), the authors studied health disparities in terms of comorbidities by gender.
To compare the comorbidities, a network analysis approach was applied. A network is comprised of a defined set of items called nodes, which are linked to each other through edges. An edge represents a defined relationship between the nodes. A very common example of network is a friendship network in which individuals are connected to each other if they are friends. Other common networks are computer networks, Web page networks, road networks, and airport networks.
To compare the comorbidities, networks of the diagnoses developed by men and women were created. The information about the diseases developed by each patient in the lifetime history was used to create a comorbidity network. For the analysis, 12 million female patients and 9.9 million male patients were used. To manage such a huge data set, Teradata Aster Big Data platform was used. To extract and prepare the network data, SQL, SQL-MR, and SQL-GR frameworks supported by Aster were utilized. To visualize the networks, Aster AppCenter and Gephi were used.
Figure 9.11 presents the female and male comorbidity networks. In these networks, nodes represent different diseases classified as the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), aggregated at the threedigit level. Two diseases are linked based on the similarity calculated using Salton Cosine Index. The larger the size of a node, the greater the comorbidity of that disease. The female comorbidity network is denser than the male network. The number of nodes and edges in the female network are 899 and 14,810, respectively, whereas the number of nodes and edges in the male network are 839 and 12,498, respectively. The visualizations present a difference between the pattern of diseases developed in male and female patients. Specifically, females have more comorbidities of mental disorders than males. On the other hand, the strength of some disease associations between lipid metabolism and chronic heart disorders is stronger in males than females. Such health disparities present questions for biological, behavioural, clinical, and policy research.
The traditional database systems would be taxed in efficiently processing such a huge data set.
The Teradata Aster made the analysis of data containing information on millions of records fairly fast and easy. Network analysis is often suggested as one method to analyze big data sets. It helps understand the data in one picture. In this application, the comorbidity network explains the relationship between diseases at one place.

Questions for Discussion
1. What could be the reasons behind the health disparities across gender?
2. What are the main components of a network?
3. What type of analytics was applied in this application?