DYNACARE: Dynamic Cardiac Arrest Risk Estimation

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Joyce Ho, Yubin Park, Carlos Carvalho, Joydeep Ghosh ;
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, PMLR 31:333-341, 2013.

Abstract

Cardiac arrest is a deadly condition caused by a sudden failure of the heart with an in-hospital mortality rate of ∼80%. Therefore, the ability to accurately estimate patients at high risk of cardiac arrest is crucial for improving the survival rate. Existing research generally fails to utilize a patient’s temporal dynamics. In this paper, we present two dynamic cardiac risk estimation models, focusing on different temporal signatures in a patient’s risk trajectory. These models can track a patient’s risk trajectory in real time, allow interpretability and predictability of a cardiac arrest event, provide an intuitive visualization to medical professionals, offer a personalized dynamic hazard function, and estimate the risk for a new patient.

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