JMIR Publications

ABSTRACT

Background:

Cardiac arrest is the most serious death-related event in intensive care units (ICUs), but it is not easily predicted due to the complex and time-dependent-data characteristics of intensive care patients. Given the complexity and time dependence of ICU data, deep learning-based methods are expected to provide a good foundation for developing risk prediction models based on large clinical records.

Objective:

The main purpose of this study is to implement a deep learning model that estimates the distribution of cardiac arrest risk probability over time based on clinical data and assesses its potential.

Methods:

A retrospective study of 759 patients in a medical ICU between January 2013 and July 2015 was conducted. A character level-gated recurrent unit (Char-GRU) with a Weibull distribution algorithm was used to develop a real time prediction model. Five-fold cross validation testing (training/validation set: 80%/20%) determined the consistency of model accuracy. The time-dependent area under the curves (TAUC) was analyzed based on the aggregation of five validation sets.

Results:

The TAUCs of the implemented model were 0.963, 0.942, 0.917, 0.875, 0.850, 0.842, and 0.761 before cardiac arrest at 1, 8, 16, 24, 32, 40, and 48 hours, respectively. The sensitivity was between 0.846 and 0.909, and specificity was between 0.923 and 0.946. The distribution of risk between the cardiac arrest group and the non-cardiac arrest group was generally different, and the difference rapidly increased as the time left until cardiac arrest reduced.

Conclusions:

A deep learning model for forecasting cardiac arrest was implemented and tested by considering the cumulative and fluctuating effects of time-dependent clinical data gathered from a large medical center. This real-time prediction model is expected to improve patient’s care by allowing early intervention in patients at high risk of unexpected cardiac arrests.