David Clifton
Professor of Clinical Machine Learning
David Clifton is Professor of Clinical Machine Learning in the Department of Engineering Science of the University of Oxford. He is a Research Fellow of the Royal Academy of Engineering, Visiting Chair in AI for Healthcare at the University of Manchester, and a Fellow of Fudan University, China. He runs the Computational Health Informatics Lab within the Department of Engineering Science, which has sites in Oxford (at the Institute of Biomedical Engineering) and China (in the Oxford Suzhou Centre for Advanced Research).
David studied Information Engineering at Oxford's Department of Engineering Science. His previous research resulted in patented systems for jet-engine health monitoring, used with the engines of the Airbus A380, the Boeing 787 "Dreamliner", and the Eurofighter Typhoon. Since 2008, he has focused mostly on healthcare applications, and his current research focuses on the development of machine learning for tracking the health of complex systems. Patents arising from his collaborative research have been commercialised via university spin-out companies OBS Medical, Oxehealth, and Sensyne Health. He holds an EPSRC "Grand Challenge" fellowship for "future leaders in healthcare", and was jointly awarded the inaugural Vice-Chancellor's Prize for Innovation, for interdisciplinary research.
Recent publications
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A distribution-based selective optimization method for eliminating periodic defects in harmonic signals
Journal article
Xin Q-Y. et al, (2023), Mechanical Systems and Signal Processing, 185, 109781 - 109781
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Graph representation learning based on deep generative gaussian mixture models
Journal article
Niknam G. et al, (2023), Neurocomputing, 523, 157 - 169
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SeroTracker-RoB: a decision rule-based algorithm for reproducible risk of bias assessment of seroprevalence studies.
Journal article
Bobrovitz N. et al, (2023), Research synthesis methods
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Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: A systematic review and meta-analysis of standardized population-based studies.
Journal article
Bergeri I. et al, (2022), PLoS medicine, 19
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Serology Assays Used in SARS-CoV-2 Seroprevalence Surveys Worldwide: A Systematic Review and Meta-Analysis of Assay Features, Testing Algorithms, and Performance.
Journal article
Ma X. et al, (2022), Vaccines, 10