I am an integrative epidemiologist who uses new informatics-based approaches, harnessing and interrogating "big health care data" from electronic medical records for the purpose of translation of stratified medicine into primary care.
I obtained a BA from the University of Virginia majoring in Biology and minoring in Economics in 2008. I worked for the consulting firm IHS Global Insight conducting pricing forecasts for pharmaceuticals, health care wages and inflation. In addition, I routinely conducted cost-effectiveness and cost-benefit analysis for new health technologies for major health care clients. I left consultancy to obtain a MPH with distinction from the University of Nottingham in 2010 and continued on to obtain a PhD in Epidemiology and Public Health from the University of Nottingham in 2013.
Stratified/Precision Medicine, "Big Data" Research, Machine-Learning, Translational Genomics, Applied Epidemiology, Risk Prediction Modelling, Health Economic Methods, Applied Statistics
My current research is focused on developing and translating risk prediction tools to support clinical decision making in primary care, using novel methods, for the purpose of stratified medicine.… read more
WENG, S.F., REPS, J., KAI, J., GARIBALDI, J.M. and QURESHI, N., 2017. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE. 12(4), e0174944 REDSELL, S.A., ROSE, J., WENG, S., ABLEWHITE, J., SWIFT, J.A., SIRIWARDENA, A.N., NATHAN, D., WHARRAD, H.J., ATKINSON, P., WATSON, V., MCMASTER, F., LAKSHMAN, R. and GLAZEBROOK, C., 2017. Digital technology to facilitate Proactive Assessment of Obesity Risk during Infancy (ProAsk): a feasibility study BMJ Open. 7(e017694), REDSELL, SA., WENG, S., SWIFT, J.A., NATHAN, D. and GLAZEBROOK, C., 2016. Validation, Optimal Threshold Determination, and Clinical Utility of the Infant Risk of Overweight Checklist for Early Prevention of Child Overweight Childhood Obesity. 12(3), 202-209
REDSELL, S.A., EDMONDS, B., SWIFT, J.A., SIRIWARDENA, A.N., WENG, S., NATHAN, D. and GLAZEBROOK, C., 2016. Systematic review of randomised controlled trials of interventions that aim to reduce the risk, either directly or indirectly, of overweight and obesity in infancy and early childhood Maternal & Child Nutrition. 12(1), 24-38
My current research is focused on developing and translating risk prediction tools to support clinical decision making in primary care, using novel methods, for the purpose of stratified medicine. These projects include:
- Investigating the impact of novel markers for cardiovascular disease prediction using large routine primary care databases (Clinical Practice Research Datalink)
- Development and implementation of the familial hypercholesterolaemia case ascertainment tool (FAMCAT) in primary care practice (Funded by the Nottingham CCG Programme Grant Development Award)
- Implementation and evaluation of the NICE familial hypercholesterolaemia guidelines in primary care (FAMCHOL Feasibility Study funded by NIHR-National School for Primary Care Research)
- Implementation and evaluation of the NICE familial breast cancer guidelines in primary care (FBC Exploratory Trial funded by NIHR-National School for Primary Care Research)
- Exploring the acceptability and feasibility of the assessing future obesity risk in infants based on early-life determinants (ProAsk Study funded by the Medical Research Council - Public Health Intervention Development Scheme)
- Developing a new framework for making inferences across genomic and clinical datasets (IPADD - Inferring Pathways Across Disparate Databases funded by the University of Nottingham 'Big Data' Competition)
- Clinical utility study for genetic testing for famlial hypercholesterolaemia in primary care using FAMCAT (NIHR-National School for Primary Care Research)
I am a member of the Primary Care Stratified Medicine (PRISM) research group.
I have obtained a NIHR-National School for Primary Care Research Fellowship commencing from October 2015 to explore novel methodologies to improve risk prediction in clinical decision tools for use in primary care. This includes:
- Evaluating whether machine-learning techniques (deep learning, neural networks) for predicting future cardiovascular disease can improve accuracy and risk classification compared to traditional epidemiological modeling
- Developing and evaluating "argumentation", a method of representing logical reasoning through evidence synthesis embedded within artificial intelligence, to improving risk stratification of familial breast cancer in primary care
- Using these novel approaches to explore the analytic validity of incorporating genomic markers for the purpose of personalized or stratified medicine.