Improving the completeness, granularity, and use of ethnicity data to minimise bias in machine learning prediction models

The aim of this work is to address healthcare inequalities through the lens of ethnicity data in electronic health records, using data linkage of primary and secondary care data to produce more complete and granular ethnicity records. This project is developing these improved groupings to use within risk prediction models that predict incident cardiovascular disease in COVID-19 patients, two diseases where ethnicity-based disparities are known to exist. Through the use of more granular ethnicity groupings and ethnicity-specific models, this work seeks to facilitate a better understanding of the use of ethnicity data in machine learning health research to avoid perpetuating existing inequalities in patient outcomes.