Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence.

ABSTRACT: Clinical research relies on high-quality patient data, however, obtaining big data sets is costly and access to existing data is often hindered by privacy and regulatory concerns. Synthetic data generation holds the promise of effectively bypassing these boundaries allowing for simplified data accessibility and the prospect of synthetic control cohorts. We employed two different methodologies of generative artificial intelligence - CTAB-GAN+ and normalizing flows (NFlow) - to synthesize patient data derived from 1606 patients with acute myeloid leukemia, a heterogeneous hematological malignancy, that were treated within four multicenter clinical trials. Both generative models accurately captured distributions of demographic, laboratory, molecular and cytogenetic variables, as well as patie

SUBMITTER: Eckardt JN 

PROVIDER: S-EPMC10954666 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature