2013 AISTATS AISTATS 2013

DYNACARE: Dynamic Cardiac Arrest Risk Estimation

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.

📈 Trend Setter — Risk Management
🧭 Keyword Pioneer — cardiac arrest
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Robotics, Speech & Audio
🌉 Interdisciplinary Bridge — Data Science & Analytics and Healthcare & Medicine and Machine Learning
🐣 Hot Topic Early Bird — temporal dynamics