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Arthritis Research UK has approved a 5-year renewal of the OA Centre.
Sara Khalid
Sara is an Associate Professor of Health Informatics and Biomedical Data Science within the Centre for Statistics in Medicine. She trained in electrical and biomedical engineering prior to founding the PHI Lab, which studies data science and artificial intelligence for planetary health.
Improving Malaria Prediction Models
Using satellite data, including vegetation levels, nighttime lights, rainfall and temperature, connected to malaria levels in South Asia to improve malaria prediction models.
How Hot Temperatures change Physical Activity
Using de-identified data from fitness trackers connected with localised weather data to study the impact of extreme temperatures on sleep and activity.
Measuring the Humanitarian Impact of Flooding
Using Satellite data to track the humanitarian impacts of flooding, particularly on Schools, Hospitals and Roads.
Meet the Team
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Our Talks
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Project Patient and Public Involvement
This project was co-designed and delivered alongside patient partners, with the aim of ensuring that the research we produced was valuable for patients and the public, with partners helping to guide us at each step of the way.
Fair and Safe Medical AI
Analysing potential biases in health-specific Large Language Models (LLMs) for global use, trained on real-world patient data.
Rare heart diseases during the COVID-19 Pandemic
Assessing how comorbidities differ between patients with rare cardiometabolic conditions versus common cardiometabolic diseases, looking for sub-groups and how these groups were impacted by the pandemic.
Osteoporosis fractures and hearing loss
Demonstrating the association between hearing loss and increased risk of osteoporosis fracture, and predicting the 1-year and 10-year risk in different patient groups.
Heart Disease in fracture-risk patients
Predicting the risk of cardiovascular disease events in populations at high risk of fracture.
Genetic causes of co-occuring conditions
Combining patient records and genetic profiles to analyse the occurrences of multimorbidity in long-term conditions and their potential causal pathways for patients over 65.
AI to detect sources of air pollution
Applying AI in a two-step process to accurately, quickly and efficiently identify unregulated brick kilns, associated with high levels of pollution and modern-day slavery, from aerial satellite images.
Environmental change and children's health
Understanding the links between relocating to different environments and health indicators such as body mass index in children.