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Our project, Machine Learning-enabled personalized medicine to improve severe COVID-19 outcomes was selected for funding of $1M by the NC Collaboratory. This is a partnership with SAS (Cary, NC) and at UNC-CH, an MPI effort with our lab, Matt Wolfgang (micro), and Rob Hagan (DoM). Individuals hospitalized with infection-related respiratory symptoms are beyond the protection of vaccines and anti-viral drugs, and there is a dire need for personalized therapies. A personalized approach or precision medicine contrasts with current clinical practice, which relies on standardized therapies that show heterogeneous benefits across large populations. One major roadblock to precision medicine related to lung infections and COVID-19, is the lack of analytical tools that can predict in real-time which therapies will be most beneficial on an individual basis. The goal of our project is to leverage our new understanding of varied host responses to guide personalized treatments using machine learning. We will merge organ-specific molecular phenotyping of diseased patients with electronic health records (EHR) using machine learning-powered data modeling and apply the resulting predictive models to improve clinical care and disease outcomes in the general population.

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