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
-
Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare.
Journal article
Thakur A. et al, (2024), NPJ Digit Med, 7
-
Comparative evaluation of large-language models and purpose-built software for medical record de-identification
Preprint
Kuo R. et al, (2024)
-
Atrial fibrillation after cardiac surgery: identifying candidate predictors through a Delphi process.
Journal article
Bedford J. et al, (2024), BMJ open, 14
-
CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks
Journal article
Molaei S. et al, (2024), Journal of Healthcare Informatics Research, 8, 555 - 575
-
Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning
Journal article
(2024), PLOS Computational Biology, 20, e1012260 - e1012260