12 March 2025
New study
New study to predict multimorbidity trajectories using linked health and social care data.
HM-AI
Health data, Multimorbidity andArtificial Intelligence Research Group
Better health through data and AI.
We use health and social care data, AI and advanced analytics to understand multimorbidity, predict disease trajectories, prevent progression and develop innovations that improve the lives of people with multiple long-term conditions.
Our research
Five pillars. One human-centred approach.
Pillar 1
AI & Digital Phenotyping
Using advanced AI to find patterns, phenotypes and trajectories, and to predict outcomes so that action can be earlier and more targeted.
Explore → Pillar 2
Population Health Intelligence
Using linked health and social care data and AI to understand multimorbidity, inequalities and unmet need.
Explore → Pillar 3
Prevention & Personalised Interventions
Translating insights into interventions that work for people with multimorbidity.
Explore → Pillar 4
Innovation & Product development
Evaluating and scaling innovations across health and social care, including NHS adoption, products and industry partnerships.
Explore → Pillar 5
Climate & Future Systems
Understanding environmental risk, sustainability and resilient models of care.
Explore →Predict
Anticipate risk and disease trajectories, and personalise care.
Prevent
Targeted, equitable interventions that prevent progression and support healthier lives.
Transform
Real-world impact at scale, towards a fairer and more sustainable future for health and social care.
Latest news
From the group
Featured publication · 8 February 2025
Machine learning phenotypes
Machine learning identifies distinct multimorbidity phenotypes in primary care.
Explore our work
We collaborate with academic partners, health and social care organisations, industry and patients.