Over 60 academics across three faculties - Health and Life Sciences, Science and Engineering, Humanities and Social Sciences, form a critical mass for interdisciplinary health and biomedical informatics research.
In establishing an interdisciplinary health informatics Network, our vision is to improve patient care and public health through the development of a learning health system that links information systems to innovative health informatics research.
We aim to revolutionise approaches to stratified medicine, public health, control of infectious diseases and clinical trials through interdisciplinary research across these three faculties.
The current focus of our Health Data Science Network falls into the following themes:
- Stratified Medicine (Led by Professor Sir Munir Pirmohamed)
- Public Health (Led by Professor David Taylor-Robinson)
- Infectious Disease (Led by Professor Sarah O'Brien and Dr Alan Radford)
- Analytics, Methods and Standards (Led by Professor Paula Williamson and Dr Keith Bodger)
- Health Informatics (Led by Professor Sarah Rodgers and Professor Iain Buchan)
The Department of Biostatistics has a sustained track record of funded health data science research, including MRC projects in joint modelling and multivariate discriminant analysis, Wellcome Trust funding for statistical genetics, and NIHR funding for biomarker-guided, registry-based and point-of-care clinical e-trials. Staff are members of the domain “Machine Learning, Quantitative Methods and Functional Genomics” for the 100K Genomes Project. The Department hosts the MRC/NIHR Trials Methodology Research Partnership, the international COMET (Core Outcome Measures in Effectiveness Trials) Initiative, and the Clinical Trials Research Centre (CTRC), all undertaking team science in areas related to health data. The CTRC are running the ISDR (Interval Screening for Diabetic Retinopathy) trial, where diagnostic data from hospitals, demographic data from GP practices and imaging data from eye-clinics are integrated to calculate an individual’s risk of developing retinopathy, and randomisation to standard or personalised risk-based screening interval performed. This study has necessitated the creation of bespoke data sharing systems as mechanisms to link these different sources of data are not presently available in our health data architecture.
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The Department of Public Health and Policy is designated a WHO Collaborating Centre for Policy Research on Determinants of Health Equity; and a member of the NIHR School for Public Health Research (SPHR) and the DH Public Health Research Consortium (PHRC). Our team, ranked 3rd for 4* impact on policy and practice in REF 2014, has multidisciplinary expertise in public health epidemiology and integral health data science disciplines including statistics, mathematics, engineering, social science, and policy. We focus on using cohort data linked to administrative data, and our work has shown that transitioning into poverty is associated with an increase in childhood social and behavioural problems.
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The Department of Computer Science was ranked 1st in the 2014 Research Excellence Framework (REF) for combined world leading and internationally excellent research in Algorithms and Artificial Intelligence. In addition to research in the core fields of Computer Science, that include algorithms and complexity, optimisation theory, computer networks, robotics, machine learning and data analytics, staff work in a number of projects combining methodologies and theories from these fields with health. Examples include the combination of game theory and cognitive psychology, neuroimaging and biostatistics to investigate social and strategic reasoning in individuals with autism, video x-ray and computer vision for automated diagnostics in patients with dysphagia, combining neural networks and biomechanics for inertia sensor data analytics, or using text mining to support evidence-based public health interventions.
Read more about the Department of Computer Science
Micheal Abaho, ‘Deep Learning for Health Text Analysis’, supervised by Professor Danushka Bollegala and Professor Paula Williamson
Tariq Al Bahhawi, ‘ Cardiovascular Epidemiology’, supervised by Professor Gregory Lip and Professor Iain Buchan
Heather Davies, ‘Using big data to quantify and characterise adverse events in primary veterinary practice’, supervised by Dr David Killick, Dr PJ Noble, and Professor Alan Radford
Anna Head, ‘Multimorbidity modelling from CPRD data’, supervised by Dr Kate Fleming, Professor Martin O’Flaherty and Dr Chris Kypridemos
Vincy Huang, ‘Public Health Simulation’, supervised by Professor Iain Buchan and Dr Chris Kypridemos
James Jones, ‘Statistical approaches for improving transplant outcome’, supervised by Professor Andy Jones and Professor Derek Middleton
Elena Koneska, ‘Design and conduct of web-based healthcare intervention trials‘, supervised by Dr Susanna Dodd, Dr Duncan Appelbe, and Professor Paula Williamson
Elpida Konsioti, ‘Detecting drug-drug interactions in spontaneous reporting data’, supervised by Professor Simon Maskell and Professor Munir Pirmohamed
Violeta Razanskaite, ‘How are outcome data used in shared decision making?’, supervised by Dr Keith Bodger, Professor Paula Williamson, and Professor Bridget Young
Conor Rosato, ‘Combining data from GP, hospital admissions, social media and store card data to predict flu epidemics and the impact on hospital bed use‘, supervised by Professor Simon Maskell and Dr John Harris
Alex Rothwell, ‘Developing machine learning software for improving diagnosis of blood cancers from flow cytometry data’, supervised by Professor Andy Jones, Dr Anthony Carter, and Dr Pete Green
David Singleton, ‘Understanding antibiotic use and resistance in companion animals using electronic health data from a large sentinel network of UK veterinary practices’, supervised by Professor Alan Radford, Professor Nicola Williams, and Dr Gina Pinchbeck