Potential Projects: Health Data Science
2023-24 MRC LID Studentships – potential projects under the Health Data Science theme.
| 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 |
| Amaya Bustinduy & Sanjeev Krishna & Bonnie Webster | Development, implementation and assessment of isothermal point-of-care molecular diagnostics for both Female Genital Schistosomiasis and Human Papillomavirus | Yes | Translational & Implementation Research; Global Health; Infectious Disease | Isothermal diagnostics; multiplex, neglected tropical diseases; schistosomiasis; human papillomavirus |
| 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 |
| Chrissy Roberts & Amaya Bustinduy | Data science platforms for enhanced analytics and monitoring of female genital schistosomiasis and related health impacts | No | Health Data Science; Infectious Disease; Global Health | Machine Learning; Technology; Female Genital Schistosomiasis; Mixed Methods; Programming; Sexual and Reproductive health |
| 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 |