Deep transfer learning for reducing health care disparities arising from biomedical data inequality.

ABSTRACT: As artificial intelligence (AI) is increasingly applied to biomedical research and clinical decisions, developing unbiased AI models that work equally well for all ethnic groups is of crucial importance to health disparity prevention and reduction. However, the biomedical data inequality between different ethnic groups is set to generate new health care disparities through data-driven, algorithm-based biomedical research and clinical decisions. Using an extensive set of machine learning experiments on cancer omics data, we find that current prevalent schemes of multiethnic machine learning are prone to generating significant model performance disparities between ethnic groups. We show that these performance disparities are caused by data inequality and data distribution discrepancies betwe

SUBMITTER: Gao Y 

PROVIDER: S-EPMC7552387 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature