Precision antimicrobial prescribing for sepsis using individualised treatment effect inference

My current research interests are in the use of integrating electronic health record information with wider data sources to aid the creation of clinical decision support systems, using machine learning to develop novel insights, digital phenotyping, earlier recognition, and risk scores. Specifically, my PhD will concern the development of a clinical decision support system to assess the risk of a bacterial infection pertinent to sepsis, to identify the pathogen causing this sepsis from electronic health record data integrated with primary & secondary care data, laboratory records, and pharmacy records, and to then prescribe an appropriate antibiotic, to both improve patient outcomes, and reduce antimicrobial resistance through antimicrobial stewardship. This will be achieved through the use of machine learning, in particular Bayesian networks. Machine learning methods will be utilised to address clinical interpretability, missing data, and longitudinal time series. I am also collaborating with the Personalised National Early Warning Score project and the Antimicrobial Resistance team, as well as the Laboratory Markers of Covid-19 severity study.