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Binary classification based methods are commonly used for designing predictive models in healthcare. A common problem in many healthcare datasets is that of imbalance, where there are far more observations in one class than the other during training. In such conditions, most classifiers do not have good predictive accuracy with respect to the under-represented class. We design a new similarity-based classifier to learn from imbalanced datasets, wherein input features are transformed using similarity with respect to a chosen subset of training points. We empirically demonstrate the superiority of our algorithm over state-of-the-art methods for imbalanced data classification in real and synthetic datasets. We also illustrate the application of our classifier in predicting Acute Respiratory Failure (ARF), a critical complication in Intensive Care Units (ICU), using semi-structured text contained in nursing notes recorded during a patient's ICU stay. Our experiments, on more than 800 patient records show that using our new classifier to learn from text- based features can effectively be used to predict ARF and, potentially, other complications in ICUs.

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.

Mechanical ventilation is an important life support tool for patients in intensive care units (ICU). For various research purposes related to patient hemodynamic and cardiopulmonary monitoring, it is important to know when a patient is on a ventilator. Unfortunately, the widely used MIMIC-II database contains results from user charted data, where the user did not always store ventilation on and off times explicitly and accurately. The resulting ventilation-related data are subject to error. Therefore, there are no simple rules to define ventilation times retrospectively for this dataset. Hence, we designed a simple set of rules to determine the ventilation times using multiple sources of mechanical ventilator-related settings and physiological measurements by expert heuristics. The rules worked well in comparison with nursing notes regarding ventilation events. We conclude that our rule sets for determining ventilation times may be useful in assisting with MIMIC-II database analysis.