Application of machine learning techniques to disease surveillance to identify risk groups for blood borne viruses and sexually transmissible infections

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Electronic medical records from general practice are used to provide clinically detailed disease surveillance data to inform public health decisions. Risk factor information is not systematically recorded making it difficult to identify risk groups using these data. This PhD will improve surveillance by applying new data science methods to de-identified electronic medical records from general practice to better identify risk groups for blood borne viruses and sexually transmissible infections.