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

Area of Science:

• Critical care medicine
• Machine learning
• Uncertainty quantification

Background:

• Modeling critical care systems requires robust uncertainty quantification due to clinical data variability.
• External factors and clinical disturbances introduce significant uncertainty into decision-making processes.
• Aleatoric and epistemic uncertainties are key challenges in developing reliable predictive models.

Purpose of the Study:

• To employ Bayesian variational autoencoders (BVAEs) for quantifying aleatoric and epistemic uncertainties in critical care models.
• To develop BVAE models capable of detecting hemorrhage incidents using out-of-distribution (OoD) data detection.
• To enhance model confidence and identify data collection needs through self-identified OoD scenarios.

Main Methods:

• Utilized Bayesian variational autoencoders (BVAEs) to model clinical data.
• Implemented OoD detection mechanisms within BVAE frameworks.
• Focused on fluid therapy scenarios to test hemorrhage detection capabilities.

Main Results:

• BVAE models successfully quantified both aleatoric and epistemic uncertainties.
• The models demonstrated proficiency in detecting OoD scenarios indicative of hemorrhage.
• Increased model uncertainty in OoD situations correlated with hemorrhage detection.

Conclusions:

• BVAEs offer a promising approach for uncertainty quantification in critical care.
• OoD detection via BVAEs serves as a reliable indicator for critical events like hemorrhage.
• Model self-awareness of uncertainty can guide future data acquisition and model improvement.