Predicting postoperative acute respiratory failure in critical care using nursing notes and physiological signals

Postoperative Acute Respiratory Failure (ARF) is a serious complication in critical care affecting patient morbidity and mortality. In this paper we investigate a novel approach to predicting ARF in critically ill patients. We study the use of two disparate sources of information – semi-structured text contained in nursing notes and investigative reports that are regularly recorded and the respiration rate, a physiological signal that is continuously monitored during a patient's ICU stay. Unlike previous works that retrospectively analyze complications, we exclude discharge summaries from our analysis envisaging a real time system that predicts ARF during the ICU stay. Our experiments, on more than 800 patient records from the MIMIC II database, demonstrate that text sources within the ICU contain strong signals for distinguishing between patients who are at risk for ARF from those who are not at risk. These results suggest that large scale systems using both structured and unstructured data recorded in critical care can be effectively used to predict complications, which in turn can lead to preemptive care with potentially improved outcomes, mortality rates and decreased length of stay and cost.