Potential Projects: Health Data Science

2023-24 MRC LID Studentships – potential projects under the Health Data Science theme.

Applicants should note that this list is incomplete. Additional projects may be added until the end of November 2022.

Supervisors Project Title Cross Institutional Supervision Theme/s Keywords
Amiya Bhatia & Daniel Carter & Karen Devries How do childhood contexts affect violence against young people as they grow up? A quantitative, longitudinal and intersectional analysis of cohort data in Uganda No Global Health; Health Data Science Social epidemiology; violence against children; causal inference; intersectionality; inequality
Helena Carreira & Harriet Forbes & Krishnan Bhaskaran Severe infection outcomes among cancer survivors in the UK No Health Data Science Electronic health records; Cancer; Infections; Outcomes research
Rosalind Eggo & Graham Medley Modelling the disorganised ecology of post-pandemic respiratory viruses No Infectious Disease; Health Data Science Mathematical modelling; Epidemiology; Respiratory viruses; Methodological novelty
Harriet Forbes & Helena Carreira & Krishnan Bhaskaran Exploring long-term health outcomes among cancer survivors No Health Data Science EHR data; cancer; statistics; mental health; cognition; quality of life
Gwen Knight & Jodi Lindsay Antimicrobial Resistance Diversity: quantification, evolution, and importance for public health Yes Infectious Disease; Health Data Science; Global Health; Translational & Implementation Research Antimicrobial resistance; mathematical modelling; MRSA; resistance diversity; intervention impact
Alex Lewin & Ruth Keogh & Sinead Langan Causal mediation with time-updated variables in Survival analysis, with application to mediation analysis of the atopic dermatitis – cardiovascular disease association No Health Data Science Causal inference; survival analysis; Bayesian methods; imputation
Finn McQuaid & Gwen Knight Comparing drug-resistant tuberculosis empirical treatment choices No Infectious Disease; Global Health; Health Data Science Tuberculosis; Drug-resistance; Modelling; Health Economics
Joan Morris & Joachim Tan & Ruth Keogh Methods for combining aggregated electronic health records data, with application to European data on children with rare congenital anomalies Yes Health Data Science Meta-analysis; Survival Analysis; R programming; Health Care Data
Lucy Pembrey & Phil Cooper & Karin Van Veldhoven Defining new asthma phenotypes using high dimensional data Yes Health Data Science; Global Health Big data; Biomarkers; Asthma; Microbiome; Epigenomics
Alicja Rudnicka & Alex Lewin & Christopher Owen AI methodology for investigating age-related cognitive changes and retinal imaging Yes Health Data Science Big data; Bayesian modelling; Machine learning; Spatial modelling
Anoop Shah & Poppy Mallinson & Alex Lewin Applying deep learning to cardiac imaging to understand the pathology of premature heart disease in South Asian populations No Health Data Science; Global Health Cardiac pathology; echocardiography; imaging; deep learning; statistics; disease mechanisms; India
Laura Southgate & Alan Pittman Navigating the diagnostic odyssey of inherited vascular disease No Health Data Science Bioinformatics; Genomics; Rare disease; Vascular disorders
John Tazare & Elizabeth Williamson Faking it: exploring how to generate synthetic electronic health record data No Health Data Science Electronic health records; machine learning; synthetic data
Elizabeth Williamson & Neil Pearce Designing optimal rapid data collection strategies to maximise the utility of routinely collected health data for predicting risk of death from an emerging infectious disease No Health Data Science; Infectious Disease Infectious disease; risk prediction; electronic health records; statistical methodology
Kevin Wing & John Tazare & Charlotte Warren-Gash Improving the ability to study epilepsy in big data No Health Data Science Epilepsy; electronic health records; case classification; trial replication
Angel Wong & Charlotte Warren-Gash Evaluate and address the impact of COVID-19 lockdown on electronic health records in causal modelling research No Health Data Science Electronic health records; pharmacoepidemiology; antibiotics; antipsychotics; COVID-19; quantitative bias analysis

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