An Acute Hypotensive Episode (AHE) is the sudden onset of a sustained period of low blood pressure and is one among the most critical conditions in Intensive Care Units (ICU). Without timely medical care, it can lead to an irreversible organ damage and death. By identifying patients at risk for AHE early, adequate medical intervention can save lives and improve patient outcomes. In this paper, we design a novel dual–boundary classification based approach for identifying patients at risk for AHE. Our algorithm uses only simple summary statistics of past Blood Pressure measurements and can be used in an online environment facilitating real–time updates and prediction. We perform extensive experiments with more than 4,500 patient records and demonstrate that our method outperforms the previous best approaches of AHE prediction. Our method can identify AHE patients two hours in advance of the onset, giving sufficient time for appropriate clinical intervention with nearly 80% sensitivity and at 95% specificity, thus having very few false positives.
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.
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