Novel risk-based monitoring solution to the data overload in intensive care medicine

Patients in the ICU generate more data than any other clinical environment. Data overload leads to preventable mortality and increased costs. Clinical decision support systems that assist clinicians with interpreting the vast amount of acquired physiologic signals have the potential to save lives and reduce cost. This paper presents a novel methodology which employs physiologic models to translate patient data into actionable risks that are relevant for informed treatment decisions. At the core of the reported technology is a particle-based inference scheme implemented using a Dynamic Bayesian Network that estimates the probabilities of specific pathologies and their causes. The methodology is demonstrated through a pilot study on a post-operative congenital single ventricle population.