Bayesian Variational Autoencoders for Out-of-Distribution Detection in Physiological Modeling: A Case Study in Fluid...

Uncertainty quantification is crucial in modeling critical care systems, where external factors such as clinical disturbances significantly impact decision-making. This study employs Bayesian variational autoencoders (BVAEs) to quantify inherent randomness in clinical data (aleatoric uncertainty) and detect uncertainty in the biases and weights of the neural network model (epistemic uncertainty). Focusing on fluid therapy, the proposed BVAE models aim to detect hemorrhage incidents through out-of-distribution (OoD) data detection. The models' ability to self-identify OoD scenarios not only provides a measure of confidence in their predictions but also highlights areas where additional data collection could enhance performance. Simulation results show promising outcomes, particularly in identifying hemorrhage through increased model uncertainty in OoD scenarios.