Unlocking Electronic Health Records for Disease Surveillance and Pandemic Intelligence: Informatics & AI-based approaches

Using COVID-19 as a case study, Chris's work seeks to harness the information contained within national-scale electronic health records for disease surveillance and pandemic intelligence. His work spans informatics and policy, such as linkage and clinical coding standards, through to machine learning tasks such as phenotyping, risk prediction and treatment effect estimation. With a clinical background in Anaesthesia and Intensive Care he is particularly interested in how domain knowledge and uncertainty can be leveraged to learn predictive representations that are both robust and interpretable.