Infection surveillance program at Medical University of South Carolina generates surplus materials, which may be suitable for microbiome analysis. We use these materials to study the role of microbiota in infection by developing predictive models using clinical patient data and microbiome composition. We further develop novel models of collaborative sharing of human microbiome specimens and data linked to electronic health records at multiple institutions to enable secure access of sequencing and patient data for investigators.
Learning Objective 1: Can we predict infections using clinical and microbiome features
Bashir Hamidi, Medical University of South Carolina
Leslie Lenert, Medical University of South Carolina
Jihad Obeid, Medical University of South Carolina
Alexander Alekseyenko (Presenter)
Medical University of South Carolina