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, 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.
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
An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction
Yang Y et al. (2021), Briefings in Bioinformatics
Interactive Effects of Heart Rate Variability and P-QRS-T on the Power Density Spectra of ECG Signals
Clifton D & Lu L (2021), IEEE Journal of Biomedical and Health Informatics
CLOCS: contrastive learning of cardiac signals across space, time, and patients
Kiyasseh D, Zhu T & Clifton D (2021), 5606-5615
Forecasting COVID-19 severity by intelligent optical fingerprinting of blood samples
Faria SP et al. (2021), Diagnostics, 11(8)
A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.
Kiyasseh D, Zhu T & Clifton D (2021), Nature Communications, 12(1)
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
An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction
Yang Y et al. (2021), Briefings in Bioinformatics
Interactive Effects of Heart Rate Variability and P-QRS-T on the Power Density Spectra of ECG Signals
Clifton D & Lu L (2021), IEEE Journal of Biomedical and Health Informatics
CLOCS: contrastive learning of cardiac signals across space, time, and patients
Kiyasseh D, Zhu T & Clifton D (2021), 5606-5615
Forecasting COVID-19 severity by intelligent optical fingerprinting of blood samples
Faria SP et al. (2021), Diagnostics, 11(8)
A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.
Kiyasseh D, Zhu T & Clifton D (2021), Nature Communications, 12(1)
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
An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction
Yang Y et al. (2021), Briefings in Bioinformatics
Interactive Effects of Heart Rate Variability and P-QRS-T on the Power Density Spectra of ECG Signals
Clifton D & Lu L (2021), IEEE Journal of Biomedical and Health Informatics
CLOCS: contrastive learning of cardiac signals across space, time, and patients
Kiyasseh D, Zhu T & Clifton D (2021), 5606-5615
Forecasting COVID-19 severity by intelligent optical fingerprinting of blood samples
Faria SP et al. (2021), Diagnostics, 11(8)
A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.
Kiyasseh D, Zhu T & Clifton D (2021), Nature Communications, 12(1)
