Amith Muhammad Tuan, Andrews Sharon, Heads Angela, Kluwe-Schiavon Bruno, Choday Atchyutha, Poonam Ramya, Ballem Sai Venkat, Tao Cui, Hamilton Jane
Department of Biostatistics and Data Science, University of Texas Medical Branch, Galveston, TX USA.
Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX USA.
Proc IEEE Int Conf Semant Comput. 2025 Feb;2025:253-258. doi: 10.1109/icsc64641.2025.00044. Epub 2025 Jun 19.
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. and 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.
电子医疗记录提供了可供挖掘和分析的数据，以改善公共卫生成果。提取的信息，特别是社交网络数据，可以帮助我们了解酒精使用障碍患者的初级保健转诊情况，并利用这些知识更好地指导对这一患者群体的服务。度中心性和介数中心性模型是可用于分析社交网络影响的两个指标。我们开发了一个核心软件库，解决了我们之前工作中的可扩展性问题。我们的库计算了大量随机生成的网络图，节点数量从500到10000个不等（约126000到4000万条边）。这个C库可以与我们之前的工作集成，以处理大量网络数据。未来计划包括为不同的网络暴露模型以及面向大型网络数据分析的接口提供支持。
