Identifying and handling data bias within primary healthcare data using synthetic data generators.

ABSTRACT: Advanced synthetic data generators can simulate data samples that closely resemble sensitive personal datasets while significantly reducing the risk of individual identification. The use of these advanced generators holds enormous potential in the medical field, as it allows for the simulation and sharing of sensitive patient data. This enables the development and rigorous validation of novel AI technologies for accurate diagnosis and efficient disease management. Despite the availability of massive ground truth datasets (such as UK-NHS databases that contain millions of patient records), the risk of biases being carried over to data generators still exists. These biases may arise from the under-representation of specific patient cohorts due to cultural sensitivities within certain communi

SUBMITTER: Draghi B 

PROVIDER: S-EPMC10823075 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature