TY - JOUR
AU - M. T. Amith
AU - S. Andrews
AU - A. Heads
AU - B. Kluwe-Schiavon
AU - A. Choday
AU - R. Poonam
AU - S. V. Ballem
AU - C. Tao
AU - J. Hamilton
A1 - 
AB - 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.
AD - 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.; College of Computing and Engineering, University of Houston Clear Lake, Houston, TX USA.; Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX USA.; Department of Artificial Intelligence and Informatics Mayo Clinic, Jacksonville, FL USA.
AN - 40600164
BT - Proc IEEE Int Conf Semant Comput
C5 - HIT & Telehealth; Opioids & Substance Use
DA - Feb
DO - 10.1109/icsc64641.2025.00044
DP - NLM
ET - 20250619
JF - Proc IEEE Int Conf Semant Comput
LA - eng
N2 - 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.
PY - 2025
SN - 2325-6516 (Print); 2325-6516
SP - 253
EP - 258+
ST - Developing a high-performing network computation of big bipartite network data toward alcohol use disorder treatment referrals
T1 - Developing a high-performing network computation of big bipartite network data toward alcohol use disorder treatment referrals
T2 - Proc IEEE Int Conf Semant Comput
TI - Developing a high-performing network computation of big bipartite network data toward alcohol use disorder treatment referrals
U1 - HIT & Telehealth; Opioids & Substance Use
U3 - 10.1109/icsc64641.2025.00044
VL - 2025
VO - 2325-6516 (Print); 2325-6516
Y1 - 2025
ER -