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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An adversarial training framework for mitigating algorithmic biases in clinical machine learning
Journal article
Yang J. et al, (2023), npj Digital Medicine, 6
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Heterogeneity in diagnosis and prognosis of ischaemic stroke subtypes: 9-year follow-up of 22000 cases in Chinese adults.
Journal article
Chun M. et al, (2023), Int J Stroke
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On the Effectiveness of Compact Biomedical Transformers.
Journal article
Rohanian O. et al, (2023), Bioinformatics
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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