Machine learning for early prediction of circulatory failure in the intensive care unit
This addresses the challenge for intensive care clinicians in processing complex patient data to detect early signs of deterioration, though it is incremental as it applies existing machine learning methods to a specific medical domain.
The researchers tackled the problem of predicting circulatory failure in ICU patients by developing a machine learning early warning system, achieving 90.0% prediction accuracy with 81.8% of events identified over two hours in advance and an AUC-ROC of 94.0%.
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision-recall curve of 63.0%. The model was externally validated in a large independent patient cohort.