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Data Diversity Theme Volunteer: Help Make Health Data Work for Everyone
Want to get involved in practical work on data diversity and health equity, alongside a friendly global community? The DSxHE Data Diversity Theme focuses on a simple problem with big consequences: many biomedical datasets don’t represent the people most affected by disease. When data is skewed, research findings may not generalise well, and tools like risk scores and predictive models can perform worse for underrepresented groups. That gap doesn’t just “happen”. It reflects d
2 days ago
Mind the gap: The case for sex and gender-aware mental health prediction models
What if we could spot the warning signs of a mental health crisis before it escalates? What if we could predict when someone is likely to relapse after treatment? And what if we could identify who might benefit most from early intervention programmes? This is the promise of clinical prediction models: tools that use information like age, medical history, and laboratory test results to estimate someone’s risk of developing a condition or needing care in the future.
Jan 13
🎥 Statistical Methods for Health Equity Webinar: Claire Coffey (University of Cambridge)🎥
We were delighted to host Claire Coffey - University of Cambridge , for the latest instalment of our webinar series on statistical methods for fairness and equity in healthcare. Topic: Are polygenic risk scores fair for cardiovascular disease risk prediction? Abstract: Polygenic risk scores (PRS) are increasingly proposed to enhance cardiovascular disease (CVD) risk prediction, particularly for individuals whose clinical risk estimates fall in intermediate ranges where tr
Dec 9, 2025
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