Generative AI Mitigates Representation Bias Using Synthetic Health Data

Representation bias in health data can lead to unfair decisions, compromising the generalisability of research findings and impeding under-represented subpopulations from benefiting from clinical discoveries. Several approaches have been developed to mitigate representation bias, ranging from simple resampling methods, such as SMOTE, to recent approaches based on generative adversarial networks (GAN). However, generating high-dimensional time-series synthetic health data remains challenging for both resampling and GAN-based approaches. In this work, we propose a novel CA-GAN architecture able to synthesise authentic, high-dimensional time series data. CA-GAN outperforms state-of-the-art methods in qualitative and quantitative evaluation while avoiding mode collapse, a significant GAN failure. We evaluate CA-GAN’s generalisability in mitigating representation bias for Black patients in two diverse, clinically relevant datasets: acute hypotension and sepsis. Finally, we show that CA-GAN generates authentic data of the minority class while faithfully maintaining the original distribution of both datasets. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement None ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The data in MIMIC-III, where we extracted our datasets from, was previously de-identified, and the institutional review boards of the Massachusetts Institute of Technology (No. 0403000206) and Beth Israel Deaconess Medical Center (2001-P-001699/14) both approved the use of the database for research. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data underlying this article are freely available in the MIMIC-III repository