Developing a high-performing network computation of big bipartite network data toward alcohol use disorder treatment referrals.

Abstract

Electronic health care records offer big data to mine and analyze towards improving public health outcomes. The information extracted, specifically social network data, could help us understand the primary care referrals for patients experiencing alcohol use disorder and wield that knowledge to better inform the engagement of this patient population. Network exposure and affiliation exposure models are two metrics that can be utilized to analyze the influence of social networks. We developed a core software library that address the scalability issue of our previous work. Our library computed high volume, randomly generated network graphs that range from 500-10,000 nodes (~126,000-40 million edges). This C library can be integrated with our previous work to handle high volume network data. Future plans include providing support for variant network exposure models and interfaces towards big network data analytics.