New Funded Project: Big Data Analytics of HIV Treatment Gaps in South Carolina

Dr. Xiaoming Li (PI) and Dr. Bankole Olatosi (PI), in collaboration with Dr. Jianjun Hu, Dr. Sharon Weissman, and Dr. Jiajia Zhang, have secured an NIH project titled, “Big Data Analytics of HIV Treatment Gaps in South Carolina: Identification and Prediction” which is funded by the National Institute of Allergy and Infectious Diseases (NIAID). The purpose of this study is to use novel machine learning techniques such as deep learning using neural networks to further explore, identify, characterize, and explain predictors of missed opportunities for HIV medical care utilization among all living HIV+ individuals in South Carolina. The public health prevention value that HIV treatment brings includes improved survival and outcomes of care among HIV+ individuals as well as reduced HIV transmission. These important components form part of the overall strategy for fighting and controlling the HIV epidemic in the United States. Using state-level CD4 and Viral Load testing data available for all SC HIV+ individuals since 2004, the study will link inpatient and outpatient claims data sources and data from the state corrections database to create a unique population-based dataset spanning 10 years (2004-2013). Advanced big data analytical techniques such as artificial neural networks, automated cluster analysis and decision tree analyses will be used to create person level profile patterns of health utilization behaviors and for identifying best predictors of linkage and retention in HIV medical care.