About us

Our work has three core research themes:

Data Science for Health Care Data
We develop and apply advanced analytical tools including predictive modelling, anomaly detection, and multimodal data integration to support diagnostics, personalised medicine, and healthcare system forecasting.

Data‑Driven Insights for Population Health
We analyse large‑scale datasets to understand population health patterns, identify disparities, and inform targeted interventions.

Data-Driven Causal Inference for Real‑World Evidence
We use linked health care data to emulate clinical trials and evaluate the real‑world effectiveness and safety of healthcare interventions, with a focus on equity and diverse populations.

What we do

1. Data Science for Health Care Data

Driving the shift toward digital, data-enabled primary care

We use advanced analytics, natural language processing, and multimodal data integration to build tools that support clinicians and improve patient journeys.

We focus on:

  • Predictive modelling and early‑warning systems
  • Integrating structured and unstructured UK health data
  • Building transparent, fair, explainable AI models

Impact:

  • Earlier identification of risk
  • More personalised and anticipatory care
  • Improved system efficiency and planning

2. Data‑Driven Insights for Population Health

Supporting the shift to prevention and neighbourhood‑level health planning

We analyse major UK datasets (UK Biobank, CPRD, SAIL, HES, ONS) to track long‑term patterns in disease, need, and service use to provide the evidence needed for preventative, proactive care.

We focus on:

  • Mapping disease trends and care pathways
  • Understanding regional, socioeconomic, and ethnic inequalities
  • Identifying opportunities for earlier diagnosis and better prevention

Impact:

  • Enables systems to plan smarter
  • Supports prevention strategies
  • Helps target resources to high‑need groups and areas

3. Data-Driven Causal Inference for Real‑World Data

Supporting personalised care and better-informed clinical decisions

We apply robust statistical approaches to understand what works in everyday primary care, especially for groups underrepresented in trials.

We focus on:

  • Emulating clinical trials using linked national datasets
  • Comparing real‑world treatment effectiveness across diverse groups
  • Studying inequalities in outcomes and safety

Impact:

  • Directly informs guidelines and personalised treatment decisions
  • Reduces unwarranted variation
  • Supports national ambitions for targeted, patient-specific care