BUPNN: Manifold Learning Regularizer-Based Blood Usage Prediction Neural Network for Blood Centers

Figure 4

How manifold regularizer loss works. Data from the same hospitals are clustered near each other in latent space due to the significant domain bias possessed by the current data. The manifold regularizer loss guides the neural network model to reduce the domain bias by pulling in neighboring nodes across hospitals to mix data from different hospitals.