Abstract
This paper presents a network science approach to investigate a health information dataset, the Sexual Acquisition and Transmission of HIV Cooperative Agreement Program (SATHCAP), to uncover hidden relationships that can be used to suggest targeted health interventions. From the data, four key target variables are chosen: HIV status, injecting drug use, homelessness, and insurance status. These target variables are converted to a graph format using four separate graph inference techniques: graphical lasso, Meinshausen Bühlmann (MB), k-Nearest Neighbors (kNN), and correlation thresholding (CT). The graphs are then clustered using four clustering methods: Louvain, Leiden, and NBR-Clust with VAT and integrity. Promising clusters are chosen using internal evaluation measures and are visualized and analyzed to identify marker attributes and key relationships. The kNN and CT inference methods are shown to give useful results when combined with NBR-Clust clustering. Examples of cluster analysis indicate that the methodology produces results that will be relevant to the public health community.
Publisher
Public Library of Science (PLoS)
Reference70 articles.
1. Organization WH, et al. Social determinants of health. WHO Regional Office for South-East Asia; 2008.
2. Iguchi M, Berry S, Ober A, Fain T, Heckathorn D, Gorbach P, et al. Sexual Acquisition and Transmission of HIV Cooperative Agreement Program (SATHCAP), 2006-2008 [United States] Restricted Use Files; 2010.
3. Respondent-driven sampling: a new approach to the study of hidden populations;DD Heckathorn;Social problems,1997
4. Nearest neighbor pattern classification;T Cover;IEEE transactions on information theory,1967
5. Sparse inverse covariance estimation with the graphical lasso;J Friedman;Biostatistics,2008
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献