DATA 
SCIENCE
FOR
HEALTH
EQUITY

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.

Share your views and help us shape the priorities of the Data Diversity theme for the next year! The Turing Way Community, & Scriberia. (2024). Illustrations from The Turing Way: Shared under CC-BY 4.0 for reuse. Zenodo. https://doi.org/10.5281/zenodo.13882307 Health datasets can fail to fully represent the populations they aim to serve. When certain groups are missing or measured inconsistently, research findings may not generalise, and data-driven tools can perform less we

by Brieuc Lehmann Inequality is part of our society, and the way we collect and analyse information about people can reinforce this...

by Katherine Liddell Engaging in the Community Diamond Innovation Network (CDIN) presents a unique opportunity for businesses to align...

by Katherine Liddell Our aim to build a preventative health network has surfaced three critical insights: the importance of technology,...

by Lizzie Remfry Whitehead Full Report: Equity in Medical Devices: An Independent Review The Whitehead review, released in March 2024,...