Short-term prediction of low kidney function in ICU patients

Intensive care treatment presents unique challenges in the medical world. When treating patients, their wide variety leave care providers with few past examples to draw on. Instead of operating in a pure knowledge discovery capacity, decision support systems can be developed to help predict short-term and long-term patient outcome, based upon available data. One area in which generalized severity scoring systems have consistently performed poorly is among patients admitted intensive care units (ICU) who then develop acute kidney injury. Urine output is used to guide fluid resuscitation and is one of the criteria for the diagnosis of acute kidney injury. This paper provides an example application for predicting short-term critical kidney function in an intensive care unit. Feature construction is performed to extract important aspects of the clinical evolution of the patient. Feature selection is performed on several patient features. Classifiers based on support vector machines and Takagi-Sugeno fuzzy models are developed to predict short-term drops in patient urine output rate. Both types of models showed comparable results, with an AUC of 78%. This shows potential in using similar classifiers to build an ICU decision support system with the goal of predicting short-term complication in the patient and augment current guidelines by anticipating treatment.