DSpace: Utilizing Data Science to Predict and Improve Health Outcomes in Pediatric HIV
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- Samuel Kyobe
The project combines longitudinal electronic medical records with 'omics data to predict the development of metabolic syndrome in HIV infected children using artificial intelligence/ machine learning techniques. In addition, the project also seeks to develop more sensitive TB diagnostics biomarkers utilizing electronic health records and multi-omics data.
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