Fuzzy Modeling for Predicting Patient Survival Rate in ICU with AKI

Up to two thirds of critically ill patients in Intensive Care Units (ICU) around the world develop Acute Kidney Injury (AKI). This complication is characterized by a reduction of kidney function and is intrinsically linked to an increased risk of mortality. AKI is also connected to higher costs in healthcare due to longer hospital stay times and ICU readmissions for affected patients. The present study developed a model capable of predicting which patients are at a larger risk of death and therefore allow for a more efficient use of hospital resources. Information regarding several physiological parameters of patients with AKI as well as data regarding their survival was used for the creation of a fuzzy model. The model achieved AUC values of up to 0.92 when using mean values for time series and analyzing a subset of patients with AKI at its most advanced stage. When analysing a general population of ICU patients, the algorithm can achieve AUC values of 0.86. This value can be compared with values obtained in similar studies of 0.85 and surpasses values obtained in traditional risk scores such as APACHE II and SOFA. A model such as this, shows the potential of using this type of predictive applications for scheduling human resources and managing monetary resources on critical patients according to their relative need for attention and treatment.