Biography
Professor David Clifton is Professor of Clinical Machine Learning and leads the Computational Health Informatics (CHI) Lab. He is OCC Fellow in AI & ML at Reuben College, a Research Fellow of the Royal Academy of Engineering, Fellow of the Alan Turing Institute, Visiting Chair in AI for Health at the University of Manchester, and a Fellow of Fudan University, China. He studied Information Engineering at Oxford's Department of Engineering Science, supervised by Professor Lionel Tarassenko CBE. His research focuses on 'AI for healthcare'.
In 2018, the CHI Lab opened its second site, in Suzhou (China), with support from the Chinese government. In 2019, the Wellcome Trust's first "Flagship Centre" was announced, which joins CHI Lab to the Oxford University Clinical Research Unit in Vietnam, focused on AI for healthcare in resource-constrained settings. In 2021, the Oxford-CityU Centre for Cardiovascular Engineering was opened, of which he is associate director.
His research has won over 35 awards; he is a Grand Challenge awardee from the UK Engineering and Physical Sciences Research Council, which is an EPSRC Fellowship that provides long-term strategic support for nine "future leaders in healthcare." He was joint winner of the inaugural "Vice-Chancellor's Innovation Prize", which identifies the best interdisciplinary research across the entirety of the University of Oxford.
Most Recent Publications
SeroTracker-RoB: a decision rule-based algorithm for reproducible risk of bias assessment of seroprevalence studies.
Bobrovitz N, Noël K, Li Z, Cao C, Deveaux G et al. (2023), Research synthesis methods
Patient Clustering for Vital Organ Failure Using ICD Code with Graph Attention
Liu Z, Hu Y, Wu X, Mertes G, Yang Y et al. (2023), IEEE Transactions on Biomedical Engineering
Self-Aware SGD: Reliable Incremental Adaptation Framework For Clinical AI Models
Thakur A, Armstrong J, Youssef A, Eyre D & Clifton DA (2023), IEEE Journal of Biomedical and Health Informatics, 1-11
Graph representation learning based on deep generative gaussian mixture models
Niknam G, Molaei S, Zare H, Clifton D & Pan S (2022), Neurocomputing, 523, 157-169
Wearable vital signs monitoring for patients with asthma: a review
Talyor L, Ding X, Clifton D & Lu HY (2022), IEEE Sensors Journal
Research Interests
Research in the Computational Health Information (CHI) Laboratory focuses on the development of real in-hospital and in-home systems for AI-driven interventions that are used in practice. Translation into low- and middle-income countries (LMICs) is a parallel research theme. The lab has a focus on non-imaging AI methods: time-series analysis, -omics data, and sensor informatics.
Please see our lab research page for details.
Most Recent Publications
SeroTracker-RoB: a decision rule-based algorithm for reproducible risk of bias assessment of seroprevalence studies.
Bobrovitz N, Noël K, Li Z, Cao C, Deveaux G et al. (2023), Research synthesis methods
Patient Clustering for Vital Organ Failure Using ICD Code with Graph Attention
Liu Z, Hu Y, Wu X, Mertes G, Yang Y et al. (2023), IEEE Transactions on Biomedical Engineering
Self-Aware SGD: Reliable Incremental Adaptation Framework For Clinical AI Models
Thakur A, Armstrong J, Youssef A, Eyre D & Clifton DA (2023), IEEE Journal of Biomedical and Health Informatics, 1-11
Graph representation learning based on deep generative gaussian mixture models
Niknam G, Molaei S, Zare H, Clifton D & Pan S (2022), Neurocomputing, 523, 157-169
Wearable vital signs monitoring for patients with asthma: a review
Talyor L, Ding X, Clifton D & Lu HY (2022), IEEE Sensors Journal
DPhil Opportunities
CHI Lab offers a wide range of complex, real-world projects in AI for healthcare. Please contact Professor Clifton for enquiries.
Most Recent Publications
SeroTracker-RoB: a decision rule-based algorithm for reproducible risk of bias assessment of seroprevalence studies.
Bobrovitz N, Noël K, Li Z, Cao C, Deveaux G et al. (2023), Research synthesis methods
Patient Clustering for Vital Organ Failure Using ICD Code with Graph Attention
Liu Z, Hu Y, Wu X, Mertes G, Yang Y et al. (2023), IEEE Transactions on Biomedical Engineering
Self-Aware SGD: Reliable Incremental Adaptation Framework For Clinical AI Models
Thakur A, Armstrong J, Youssef A, Eyre D & Clifton DA (2023), IEEE Journal of Biomedical and Health Informatics, 1-11
Graph representation learning based on deep generative gaussian mixture models
Niknam G, Molaei S, Zare H, Clifton D & Pan S (2022), Neurocomputing, 523, 157-169
Wearable vital signs monitoring for patients with asthma: a review
Talyor L, Ding X, Clifton D & Lu HY (2022), IEEE Sensors Journal
