Learning from different perspectives: Robust cardiac arrest prediction via temporal transfer learning.

Predicting and preventing cardiac arrest is one of the biggest challenges of contemporary cardiology, as a patients survival depends on the effectiveness of the emergency response teams. While black-box models have shown to have better predictive accuracies for cardiac risk stratification, early warning scoring systems are more prominent in the hospital setting due to their ease of implementation and interpretability. We propose a temporal transfer learning approach to utilize information from adjacent time points to yield an early cardiac arrest prediction model that is robust in predictive accuracies as well as maintains the interpretability of the model coefficients. Our model estimates the logistic regression coefficients simultaneously for various time points to share knowledge from different observation windows. This framework can overcome small sample size issues, and result in robust estimation of the model coefficients. We find that our model consistently outperforms a logistic regression model fit only on vital sign data from a single time slice for 763 intensive care unit patients. Moreover, we find that the estimated coefficients from our model captures temporal trends in the data.