ALFADIAB – Algorithmic Fairness in Diabetes Prediction
In the EU, health inequalities account for 20% of total healthcare costs and related welfare losses amount to nearly 1 trillion EUR per year. The European Commission considers health inequalities to be one of the greatest challenges facing European healthcare systems. Navigating this challenge requires improved data and smarter methods and tools for evaluating inequalities in health as well as practical ways for narrowing healthcare gaps. The vision of the Algorithmic Fairness in Diabetes Prediction (ALFADIAB) research program is a society where access to healthcare and quality of care do not depend on ethnicity, race, sex, or wealth. Even in Denmark, with its universal healthcare system, this is not yet a reality, and minorities with diabetes, and those who are the poorest, are affected more than others. As an example, immigrants, their descendants, and those who are the poorest have higher rates of developing type 2 diabetes, experience more severe complications (diseases of the heart, eye, and kidney), and benefit less from the Danish healthcare system. In this research program, I will investigate whether established risk prediction models, that are used to forecast which individuals are at high risk of diabetes, are underperforming for minorities and those with lower socioeconomic status. By utilizing Danish registry-based data on millions of people I will assess inequalities in diabetes management and care, and deploy artificial intelligence techniques to develop improved predictive models that are equitable and perform equally well between subgroups.