Generative AI mitigates representation bias and improves model fairness through synthetic health data
Fig 5
Proposed architecture of our CA-GAN. The Generator and the Discriminator are two deep networks with similar structure and number of parameters.
Both employ three stacked Bidirectional LSTMs (BILSTMs) to capture the temporal relationships of longitudinal data. They are trained together adversarially, with a minimax game. Conditioning is achieved with static labels, passed as input to both networks. The Generator also takes Gaussian noise as input and generates time-series data (synthetic patients). The discriminator evaluates the plausibility of the output of the Generator, compared with the real data.